Physical Sciences and Mathematics Commons^™

The Assessment Of Technology Adoption Interventions And Outcome Achievement Related To The Use Of A Clinical Research Data Warehouse, Katie A. Mccarthy

Theses and Dissertations

Introduction: While funding for research has declined since 2004, the need for rapid, innovative, and lifesaving clinical and translational research has never been greater due to the rise in chronic health conditions, which have resulted in lower life expectancy and higher rates of mortality and adverse outcomes. Finding effective diagnostic and treatment methods to address the complex challenges in individual and population health will require a team science approach, creating the need for multidisciplinary collaboration among practitioners and researchers.

To address this need, the National Institutes of Health (NIH) created the Clinical and Translational Science Awards (CTSA) program. The CTSA …

Cure: Flexible Categorical Data Representation By Hierarchical Coupling Learning, Songlei Jian, Guansong Pang, Longbing Cao, Kai Lu, Hang Gao

Research Collection School Of Computing and Information Systems

The representation of categorical data with hierarchical value coupling relationships (i.e., various value-to-value cluster interactions) is very critical yet challenging for capturing complex data characteristics in learning tasks. This paper proposes a novel and flexible coupled unsupervised categorical data representation (CURE) framework, which not only captures the hierarchical couplings but is also flexible enough to be instantiated for contrastive learning tasks. CURE first learns the value clusters of different granularities based on multiple value coupling functions and then learns the value representation from the couplings between the obtained value clusters. With two complementary value coupling functions, CURE is instantiated into …

Adaptive Resonance Theory (Art) For Social Media Analytics, Lei Meng, Ah-Hwee Tan, Donald C. Ii Wunsch

Research Collection School Of Computing and Information Systems

The last decade has witnessed how social media in the era of Web 2.0 reshapes the way people communicate, interact, and entertain in daily life and incubates the prosperity of various user-centric platforms, such as social networking, question answering, massive open online courses (MOOC), and e-commerce platforms. The available rich user-generated multimedia data on the web has evolved traditional ways of understanding multimedia research and has led to numerous emerging topics on human-centric analytics and services, such as user profiling, social network mining, crowd behavior analysis, and personalized recommendation. Clustering, as an important tool for mining information groups and in-group …

Clustering And Its Extensions In The Social Media Domain, Lei Meng, Ah-Hwee Tan, Donald C. Wunsch

Research Collection School Of Computing and Information Systems

This chapter summarizes existing clustering and related approaches for the identified challenges as described in Sect. 1.2 and presents the key branches of social media mining applications where clustering holds a potential. Specifically, several important types of clustering algorithms are first illustrated, including clustering, semi-supervised clustering, heterogeneous data co-clustering, and online clustering. Subsequently, Sect. 2.5 presents a review on existing techniques that help decide the value of the predefined number of clusters (required by most clustering algorithms) automatically and highlights the clustering algorithms that do not require such a parameter. It better illustrates the challenge of input parameter sensitivity of …

Project Sidewalk: A Web-Based Crowdsourcing Tool For Collecting Sidewalk Accessibility Data At Scale, Manaswi Saha, Michael Saugstad, Hanuma Maddali, Aileen Zeng, Ryan Holland, Steven Bower, Aditya Dash, Sage Chen, Anthony Li, Kotaro Hara, Jon Froehlich

Research Collection School Of Computing and Information Systems

We introduce Project Sidewalk, a new web-based tool that enables online crowdworkers to remotely label pedestrian-related accessibility problems by virtually walking through city streets in Google Street View. To train, engage, and sustain users, we apply basic game design principles such as interactive onboarding, mission-based tasks, and progress dashboards. In an 18-month deployment study, 797 online users contributed 205,385 labels and audited 2,941 miles of Washington DC streets. We compare behavioral and labeling quality differences between paid crowdworkers and volunteers, investigate the effects of label type, label severity, and majority vote on accuracy, and analyze common labeling errors. To complement …

Community Discovery In Heterogeneous Social Networks, Lei Meng, Ah-Hwee Tan, Donald C. Wunsch

Research Collection School Of Computing and Information Systems

Discovering social communities of web users through clustering analysis of heterogeneous link associations has drawn much attention. However, existing approaches typically require the number of clusters a priori, do not address the weighting problem for fusing heterogeneous types of links, and have a heavy computational cost. This chapter studies the commonly used social links of users and explores the feasibility of the proposed heterogeneous data co-clustering algorithm GHF-ART, as introduced in Sect. 3.6, for discovering user communities in social networks. Contrary to the existing algorithms proposed for this task, GHF-ART performs real-time matching of patterns and one-pass learning, which guarantees …

Querying Over Encrypted Databases In A Cloud Environment, Jake Douglas

Boise State University Theses and Dissertations

The adoption of cloud computing has created a huge shift in where data is processed and stored. Increasingly, organizations opt to store their data outside of their own network to gain the benefits offered by shared cloud resources. With these benefits also come risks; namely, another organization has access to all of the data. A malicious insider at the cloud services provider could steal any personal information contained on the cloud or could use the data for the cloud service provider's business advantage. By encrypting the data, some of these risks can be mitigated. Unfortunately, encrypting the data also means …

Neural Multimodal Belief Tracker With Adaptive Attention For Dialogue Systems, Zheng Zhang, Lizi Liao, Minlie Huang, Xiaoyan Zhu, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Multimodal dialogue systems are attracting increasing attention with a more natural and informative way for human-computer interaction. As one of its core components, the belief tracker estimates the user's goal at each step of the dialogue and provides a direct way to validate the ability of dialogue understanding. However, existing studies on belief trackers are largely limited to textual modality, which cannot be easily extended to capture the rich semantics in multimodal systems such as those with product images. For example, in fashion domain, the visual appearance of clothes play a crucial role in understanding the user's intention. In this …

Socially-Enriched Multimedia Data Co-Clustering, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Heterogeneous data co-clustering is a commonly used technique for tapping the rich meta-information of multimedia web documents, including category, annotation, and description, for associative discovery. However, most co-clustering methods proposed for heterogeneous data do not consider the representation problem of short and noisy text and their performance is limited by the empirical weighting of the multimodal features. This chapter explains how to use the Generalized Heterogeneous Fusion Adaptive Resonance Theory (GHF-ART) generalized heterogeneous fusion adaptive resonance theory for clustering large-scale web multimedia documents. Specifically, GHF-ART is designed to handle multimedia data with an arbitrarily rich level of meta-information. For handling …

Concluding Remarks, Lei Meng, Ah-Hwee Tan, Donald C. Wunsch

Research Collection School Of Computing and Information Systems

This chapter summarizes the major contributions in this book and discusses their possible positions and requirements in some future scenarios. Section 8.1 follows the book structure to revisit the key contributions of this book in both theories and applications. The developed algorithms, such as the VA-ARTs for hyperparameter adaptation and the GHF-ART for multimedia representation and fusion, and the four applications, such as clustering and retrieving socially enriched multimedia data, are concentrated using one paragraph and three paragraphs, respectively. In Sect. 8.2, the roles of the proposed ART-embodied algorithms in social media clustering tasks are highlighted, and their possible evolutions …

Yelp Improved : Aggregating Restaurant Reviews, Kunal Sonar

Creative Activity and Research Day - CARD

In the near future, online food delivery service companies would occupy a big market share in the food industry. This project aims to provide factual information from customer reviews as part of the numerous innovations in place to drive business and demands. Natural Language Processing is used to provide a comprehensive view of individual restaurants using technologies like NLTK, SpaCy, Gensim and Sklearn. Data of one million Las Vegas restaurant customer reviews is curated from the Yelp Dataset Challenge. Reviews are pre-processed, split into chunks of phrases and mapped to attributes like food, budget, service etc. These attributes are derived …

Building Consumer Trust In The Cloud: An Experimental Analysis Of The Cloud Trust Label Approach, Lisa Van Der Werff, Grace Fox, Ieva Masevic, Vincent C. Emeakaroha, John P. Morrison, Theo Lynn

Department of Computer Science Publications

The lack of transparency surrounding cloud service provision makes it difficult for consumers to make knowledge based purchasing decisions. As a result, consumer trust has become a major impediment to cloud computing adoption. Cloud Trust Labels represent a means of communicating relevant service and security information to potential customers on the cloud service provided, thereby facilitating informed decision making. This research investigates the potential of a Cloud Trust Label system to overcome the trust barrier. Specifically, it examines the impact of a Cloud Trust Label on consumer perceptions of a service and cloud service provider trustworthiness and trust in the …

Implementation Considerations For The Digital Bronco Id, Bryan Gilginas

Honors Theses

This paper aims to discuss the conditions and preferences of students that Western Michigan University should take if they ever implement a Digital Bronco ID. These conditions are found via an anonymous survey given to random students. These students were prompted to answer questions based on their preference and possible uses of the Digital Bronco ID. It was found that the respondents were significantly diverse in their answers. However, things such as gender, major, and age range played a significant role in patterns in which students chose their preferences. Within the paper, these patterns are interpreted and discussed for the …

Cooperation In Community Colleges, Frederic S. Gore

USF Tampa Graduate Theses and Dissertations

With the mounting pressures on institutions of higher education to do more with limited resources, the opportunity to collaborate with other colleges has emerged as a viable tool to create efficiencies and obtain valuable knowledge otherwise unattainable by an institution, even if that collaboration takes place with a competing institution. Enterprise resource planning (ERP) systems are critical to managing student information and college operations, but can be challenging for colleges to implement. Consortia present a unique solution to colleges to address gaps in their expertise and skills needed to achieve a successful ERP implementation. This study explores the factors that …

Google Trends Data As A Proxy For Interest In Leadership, Finley W. Walker

Doctor of Education (Ed.D)

The purpose of this quantitative study was to investigate the observable patterns of online search behavior in the topic of leadership using Google Trends data. Institutions have had a historically difficult time predicting good leadership candidates. Better predictions can be made by using the big data offered by groups such as Google to learn who, where, and when people are interested in leadership. The study utilized descriptive, comparative, and correlative methodologies to study Google users’ interest in leadership from 2004 to 2017. Society has placed great value into leadership throughout history, and though overall interest remains strong, it appears that …

Big Data And The Consumer, Seema Chokshi

MITB Thought Leadership Series

What is big data? The intuitive meaning of the phrase ‘big data’ might be “data that is huge in quantity”. But is that interpretation enough? Data of this type has existed for as long as humans have made records of their work. Some of the earliest writings, such as cuneiform, contain vast amounts of data covering areas as diverse as law, mapping and mathematical equations.

Maximizing Multifaceted Network Influence, Yuchen Li, Ju Fan, George V. Ovchinnikov, Panagiotis Karras

Research Collection School Of Computing and Information Systems

An information dissemination campaign is often multifaceted, involving several facets or pieces of information disseminating from different sources. The question then arises, how should we assign such pieces to eligible sources so as to achieve the best viral dissemination results? Past research has studied the problem of Influence Maximization (IM), which is to select a set of k promoters that maximizes the expected reach of a message over a network. However, in this classical IM problem, each promoter spreads out the same unitary piece of information. In this paper, we propose the Optimal Influential Pieces Assignment (OIPA) problem, which is …

Modeling Sequential And Basket-Oriented Associations For Top-K Recommendation, Duc-Trong Le Duc Trong

Dissertations and Theses Collection (Open Access)

Top-K recommendation is a typical task in Recommender Systems. In traditional approaches, it mainly relies on the modeling of user-item associations, which emphasizes the user-specific factor or personalization. Here, we investigate another direction that models item-item associations, especially with the notions of sequence-aware and basket-level adoptions . Sequences are created by sorting item adoptions chronologically. The associations between items along sequences, referred to as “sequential associations”, indicate the influence of the preceding adoptions on the following adoptions. Considering a basket of items consumed at the same time step (e.g., a session, a day), “basket-oriented associations” imply correlative dependencies among these …

Question Answering With Textual Sequence Matching, Shuohang Wang

Dissertations and Theses Collection (Open Access)

Question answering (QA) is one of the most important applications in natural language processing. With the explosive text data from the Internet, intelligently getting answers of questions will help humans more efficiently collect useful information. My research in this thesis mainly focuses on solving question answering problem with textual sequence matching model which is to build vectorized representations for pairs of text sequences to enable better reasoning. And our thesis consists of three major parts.

In Part I, we propose two general models for building vectorized representations over a pair of sentences, which can be directly used to solve the …

Efficient Algorithms For Solving Aggregate Keyword Routing Problems, Qize Jiang, Weiwei Sun, Baihua Zheng, Kunjie Chen

Research Collection School Of Computing and Information Systems

With the emergence of smart phones and the popularity of GPS, the number of point of interest (POIs) is growing rapidly and spatial keyword search based on POIs has attracted significant attention. In this paper, we study a more sophistic type of spatial keyword searches that considers multiple query points and multiple query keywords, namely Aggregate Keyword Routing (AKR). AKR looks for an aggregate point m together with routes from each query point to m. The aggregate point has to satisfy the aggregate keywords, the routes from query points to the aggregate point have to pass POIs in order to …

Automatic Short Answer Grading Using Siamese Bidirectional Lstm Based Regression, Arya Prabhudesai, Nguyen Binh Duong Ta

Research Collection School Of Computing and Information Systems

Automatic student assessment plays an important role in education - it provides instant feedback to learners, and at the same time reduces tedious grading workload for instructors. In this paper, we investigate new machine learning techniques for automatic short answer grading (ASAG). The ASAG problem mainly involves assessing short, natural language responses to given questions automatically. While current research in the field has focused either on feature engineering or deep learning, we propose a new approach which combines the advantages of both. More specifically, we propose a Siamese Bidirectional LSTM Neural Network based Regressor in conjunction with handcrafted features for …

Alpha Insurance: A Predictive Analytics Case To Analyze Automobile Insurance Fraud Using Sas Enterprise Miner (Tm), Richard Mccarthy, Wendy Ceccucci, Mary Mccarthy, Leila Halawi

Publications

Automobile Insurance fraud costs the insurance industry billions of dollars annually. This case study addresses claim fraud based on data extracted from Alpha Insurance’s automobile claim database. Students are provided the business problem and data sets. Initially, the students are required to develop their hypotheses and analyze the data. This includes identification of any missing or inaccurate data values and outliers as well as evaluation of the 22 variables. Next students will develop and optimize their predictive models using five techniques: regression, decision tree, neural network, gradient boosting, and ensemble. Then students will determine which model is the best fit …

Online Collaborative Filtering With Implicit Feedback, Jianwen Yin, Chenghao Liu, Jundong Li, Bing Tian Dai, Yun-Chen Chen, Min Wu, Jianling Sun

Research Collection School Of Computing and Information Systems

Studying recommender systems with implicit feedback has become increasingly important. However, most existing works are designed in an offline setting while online recommendation is quite challenging due to the one-class nature of implicit feedback. In this paper, we propose an online collaborative filtering method for implicit feedback. We highlight three critical issues of existing works. First, when positive feedback arrives sequentially, if we treat all the other missing items for this given user as the negative samples, the mis-classified items will incur a large deviation since some items might appear as the positive feedback in the subsequent rounds. Second, the …

Evaluating Machine Learning Techniques For Smart Home Device Classification, Angelito E. Aragon Jr.

Theses and Dissertations

Smart devices in the Internet of Things (IoT) have transformed the management of personal and industrial spaces. Leveraging inexpensive computing, smart devices enable remote sensing and automated control over a diverse range of processes. Even as IoT devices provide numerous benefits, it is vital that their emerging security implications are studied. IoT device design typically focuses on cost efficiency and time to market, leading to limited built-in encryption, questionable supply chains, and poor data security. In a 2017 report, the United States Government Accountability Office recommended that the Department of Defense investigate the risks IoT devices pose to operations security, …

Imitating Human Responses Via A Dual-Process Model Approach, Matthew A. Grimm

Theses and Dissertations

Human-autonomous system teaming is becoming more prevalent in the Air Force and in society. Often, the concept of a shared mental model is discussed as a means to enhance collaborative work arrangements between a human and an autonomous system. The idea being that when the models are aligned, the team is more productive due to an increase in trust, predictability, and apparent understanding. This research presents the Dual-Process Model using multivariate normal probability density functions (DPM-MN), which is a cognitive architecture algorithm based on the psychological dual-process theory. The dual-process theory proposes a bipartite decision-making process in people. It labels …

Testing The Fault Tolerance Of A Wide Area Backup Protection System Using Spin, Kenneth James

Theses and Dissertations

Cyber-physical systems are increasingly prevalent in daily life. Smart grids in particular are becoming more interconnected and autonomously operated. Despite the advantages, new challenges arise in the form of defending these assets. Recent studies reveal that small-scale, coordinated cyber-attacks on only a few substations across the U.S. could result in cascading failures affecting the entire nation. In support of defending critical infrastructure, this thesis tests the fault tolerance of a backup protection system. Each transmission line in the system incorporates autonomous agents which monitor the status of the line and make decisions regarding the safety of the grid. Various malfunctions …

Examining Medline Search Query Reproducibility And Resulting Variation In Search Results, C. Sean Burns, Robert M. Shapiro Ii, Tyler Nix, Jeffrey T. Huber

Information Science Faculty Publications

The MEDLINE database is publicly available through the National Library of Medicine’s PubMed but the data file itself is also licensed to a number of vendors, who may offer their versions to institutional and other parties as part of a database platform. These vendors provide their own interface to the MEDLINE file and offer other technologies that attempt to make their version useful to subscribers. However, little is known about how vendor platforms ingest and interact with MEDLINE data files, nor how these changes influence the construction of search queries and the results they produce. This poster presents a longitudinal …

Evaluating An Electronic Protocol In A Pediatric Intensive Care Unit, Jeanette Rose

UNO Student Research and Creative Activity Fair

A team of clinicians at Children’s Hospital and Medical Center (CHMC) developed a standardized protocol in 2018 for the care of patients needing sedation. This protocol is ordered through the EPIC electronic health record system for patients in the pediatric intensive care unit (PICU). When used, electronic protocols reduce the variation in clinical decision making which can ultimately improve patient outcomes. The goal of this project is to evaluate this technology, how the protocol is being used, and how it may be improved. Actual users of the EPIC sedation protocol were the subjects of this study, including PICU physicians, physician …

Forensics Analysis For Bone Pair Matching Using Bipartite Graphs In Commingled Remains, Ryan Ernst

UNO Student Research and Creative Activity Fair

Identification of missing prisoners of war is a complex and time consuming task. There are many missing soldiers whose remains have yet to be returned to their families and loved ones. This nation has a solemn obligation to its soldiers and their families who have made the ultimate sacrifice for their country. There are currently over 82,000 unidentified prisoners of war which are identified at a rate of 100+ per year. At this rate it would take 300+ years to complete the identification process. Previously, anthropologists used excel spreadsheets to sort through skeletal data. This project aims to streamline the …

Welcome Message From The General Chairs, Nabil I. Alshurafa, Archan Misra, Abhishek Mukherji

Research Collection School Of Computing and Information Systems

No abstract provided.