Data Science Commons^™

Surviving Under The Reign Of El Niño Southern Oscillation: An Analysis Of The Effects Of Extreme El Niño Events On The Oceanographic And Biological Environment Of The Galápagos Islands, Ava Mcilvaine

Independent Study Project (ISP) Collection

El Niño Southern Oscillation (ENSO) is commonly known as the atmospheric and oceanographic powerhouse of the Southern Pacific Ocean. From phytoplankton to apex predators, ENSO controls the stability of species’ populations within this biodiverse ocean environment. El Niño’s 9-15 month alteration of the heat storage in the tropical Pacific drastically shifts the temperature, nutrient, and circulation gradient its marine life is accustomed to. A single degree change in the ocean’s surface layer temperature can have large consequences for marine species, and El Niño is commonly associated with Sea Surface Temperature Anomalies (SSTAs) between 2°- 6° Celsius. The potential danger El …

An Apex Predator In Peril In The Western Lowlands Of Ecuador: Mapping The Population Distribution Of Harpy Eagles (Harpia Harpyja) In A Highly Deforested Region, Samuel Zhang

Independent Study Project (ISP) Collection

The Harpy Eagle (Harpia harpyja) is a highly threatened bird of prey in Ecuador. While they are already elusive in the Ecuadorian Amazon, they are even lesser known in the coastal lowlands, and their existence is threatened by rapid deforestation. This study mapped their potential distribution by examining satellite images to find intact humid forest, their ideal habitat. Habitat areas were quantified using ImageJ. The only sites found to be adequate for sustaining Harpy Eagle populations were the primary forests in the vicinities of Reserva Ecológica Mache Chindul and Reserva Ecológica Cotacachi Cayapas. The two reserves are expected to be …

A Semi-Automatic Integrated Framework For Non-English Sentiment Lexicons, Abdullah Kaity Mohammed Salem

Student Works (2020-2029)

There has been significant growth in social media networks in the last few years. Posting opinions and messages on social networking websites has become a popular activity on the Internet. The data sources are necessary for business intelligence and market analytics, as human opinions form a major indicator of human desires and behaviour. This has resulted in the development of a new study field called sentiment analysis. This includes the analysis, evaluation and interpretation of the opinions with the help of text mining and Natural Language Processing (NLP) processes, for identifying the text polarity, as positive, neutral or negative. It …

Designing A Smart Internet Of Things Solution For Point Of Use Water Filtration Management System In Residential, Commercial And Public Settings, Tristan Lim, Hwee-Pink Tan, Chin Sin Ong, Rahul Belani, Siddhant S. K. Agrawal

Research Collection School Of Computing and Information Systems

The use of water filtration Point-of-Use (POU) systems are extensive, ranging from water dispensers in public estates, to household POU water systems. Manufacturers typically recommend filtration cartridges to be changed (i) after their useful life, or (ii) when the water flow volume have exceeded certain capacity, whichever is earlier. However, filtration mechanisms are typically not changed with sufficient regularity. Overused filters can result in negative health effects, over and above the deterioration and loss of filtration benefits of the POU water system. Presently most existing water purification systems do not have smart connected Internet of Things (IoT) means of informing …

Predictive Task Assignment In Spatial Crowdsourcing: A Data-Driven Approach, Yan Zhao, Kai Zheng, Yue Cui, Han Su, Feida Zhu, Xiaofang Zhou

Research Collection School Of Computing and Information Systems

With the rapid development of mobile networks and the widespread usage of mobile devices, spatial crowdsourcing, which refers to assigning location-based tasks to moving workers, has drawn increasing attention. One of the major issues in spatial crowdsourcing is task assignment, which allocates tasks to appropriate workers. However, existing works generally assume the static offline scenarios, where the spatio-temporal information of all the workers and tasks is determined and known a priori. Ignorance of the dynamic spatio-temporal distributions of workers and tasks can often lead to poor assignment results. In this work we study a novel spatial crowdsourcing problem, namely Predictive …

Two (Or More) Viruses In One Bat: A Systematic Quantitative Literature Review Of Viral Coinfection In Bats, Eli J. Kaufman

Independent Study Project (ISP) Collection

Viral coinfection is an important topic in pathogen dynamics, and can increase viral shedding and change disease outcomes. As bats are carriers of important zoonoses, such as the SARS coronaviruses, rabies, and other deadly viruses, knowing more about their coinfection dynamics is important. This quantitative systematic literature review sought to show how many papers reported bat viral coinfections, and created three databases. The first database, the SQLR database was based on searches for coinfections. The second database, the Astrovirus database was to determine how much of the literature was being missed by examining a single viral family more in depth …

On The Robustness Of Cascade Diffusion Under Node Attacks, Alvis Logins, Yuchen Li, Panagiotis Karras

Research Collection School Of Computing and Information Systems

How can we assess a network's ability to maintain its functionality under attacks? Network robustness has been studied extensively in the case of deterministic networks. However, applications such as online information diffusion and the behavior of networked public raise a question of robustness in probabilistic networks. We propose three novel robustness measures for networks hosting a diffusion under the Independent Cascade (IC) model, susceptible to node attacks. The outcome of such a process depends on the selection of its initiators, or seeds, by the seeder, as well as on two factors outside the seeder's discretion: the attack strategy and the …

Neural Network Pruning For Ecg Arrhythmia Classification, Isaac E. Labarge

Master's Theses

Convolutional Neural Networks (CNNs) are a widely accepted means of solving complex classification and detection problems in imaging and speech. However, problem complexity often leads to considerable increases in computation and parameter storage costs. Many successful attempts have been made in effectively reducing these overheads by pruning and compressing large CNNs with only a slight decline in model accuracy. In this study, two pruning methods are implemented and compared on the CIFAR-10 database and an ECG arrhythmia classification task. Each pruning method employs a pruning phase interleaved with a finetuning phase. It is shown that when performing the scale-factor pruning …

High Performance And Machine Learning Algorithms For Brain Fmri Data, Taban Eslami

Dissertations

Brain disorders are very difficult to diagnose for reasons such as overlapping nature of symptoms, individual differences in brain structure, lack of medical tests and unknown causes of some disorders. The current psychiatric diagnostic process is based on behavioral observation and may be prone to misdiagnosis.

Noninvasive brain imaging technologies such as Magnetic Resonance Imaging (MRI) and functional Magnetic Resonance Imaging (fMRI) make the process of understanding the structure and function of the brain easier. Quantitative analysis of brain imaging data using machine learning and data mining techniques can be advantageous not only to increase the accuracy of brain disorder …

Efficient Model-Data Integration For Flexible Modeling, Parameter Analysis & Visualization, And Data Management, Angela Gregory, Chao Chen, Rui Wi, Sarah Miller, Sajjad Ahmad, John W. Anderson, Hays Berrett, Karl Benedict, Dan Cadol, Sergiu M. Dascalu, Donna Delparte, Lynn Fenstermaker, Sarah Godsey, Frederick C. Harris Jr., James P. Mcnamara, Scott W. Tyler, John Savickas, Luke Sheneman, Mark Stone, Matthew A. Turner

Civil and Environmental Engineering and Construction Faculty Research

Due to the complexity and heterogeneity inherent to the hydrologic cycle, the modeling of physical water processes has historically and inevitably been characterized by a broad spectrum of disciplines including data management, visualization, and statistical analyses. This is further complicated by the sub-disciplines within the water science community, where specific aspects of water processes are modeled independently with simplification and model boundary integration receiving little attention. This can hinder current and future research efforts to understand, explore, and advance water science. We developed the Virtual Watershed Platform to improve understanding of hydrologic processes and more generally streamline model-data integration and …

The Analytics Managers Ultimate Guide For Working With Universities, Robert J. Mcgrath

Faculty Publications

The challenges organizations are having related to finding (and retaining) deep analytical talent did not materialize out of thin air…or overnight. Analytics and Data science – and the role of the analytics professional – has evolved over the last several decades and has been fueled by our ability to capture and process increasingly larger and more complex variations of data and our desire to gain increasingly granular insights to fuel innovation and creativity. While many organizations recognize that a partnership with a university can be a resource to many of these challenges, the best way to start a conversation with …

Development Of An International Data Repository And Research Resource: The Prospective Studies Of Acute Child Trauma And Recovery (Pact/R) Data Archive, Nancy Kassam-Adams, Justin A. Kenardy, Douglas L. Delahanty, Meghan L. Marsac, Richard Meiser-Stedman, Reginald D. V. Nixon, Markus A. Landolt, Patrick A. Palmieri

Pediatrics Faculty Publications

Background: Studies that identify children after acute trauma and prospectively track risk/protective factors and trauma responses over time are resource-intensive; small sample sizes often limit power and generalizability. The Prospective studies of Acute Child Trauma and Recovery (PACT/R) Data Archive was created to facilitate more robust integrative cross-study data analyses.

Objectives: To (a) describe creation of this research resource, including harmonization of key variables; (b) describe key study- and participant-level variables; and (c) examine retention to follow-up across studies.

Methods: For the first 30 studies in the Archive, we described study-level (design factors, retention rates) and participant-level …

Identification And Description Of Potentially Influential Social Network Members Using The Strategic Player Approach, Miles Q. Ott, Sara G. Balestrieri, Graham Diguiseppi, Melissa A. Clark, Michael Bernstein, Sarah Helseth, Nancy P. Barnett

Statistical and Data Sciences: Faculty Publications

Background: Diffusion of innovations theory posits that ideas and behaviors can be spread through social network ties. In intervention work, intervening upon certain network members may lead to intervention effects “diffusing” into the network to affect the behavior of network members who did not receive the intervention. The strategic players (SP) method, an extension of Borgatti’s Key Players approach, is used to balance the (sometimes) opposing goals of spreading the intervention to as many members of the target group as possible, while preventing the spread of the intervention to others. Objectives: We sought to test whether members of the SP …

Finding Datasets In Publications: The Syracuse University Approach, Tong Zeng, Daniel E. Acuna

School of Information Studies - Faculty Scholarship

Datasets are critical for scientific research, playing a role in replication, reproducibility, and efficiency. Researchers have recently shown that datasets are becoming more important for science to function properly, even serving as artifacts of study themselves. However, citing datasets is not a common or standard practice in spite of recent efforts by data repositories and funding agencies. This greatly affects our ability to track their usage and importance. A potential solution to this problem is to automatically extract dataset mentions from scientific articles. In this work, we propose to achieve such extraction by using a neural network based on a …

Differential Privacy Techniques For Cyber Physical Systems: A Survey, Muneeb Ul Hassan, Mubashir Husain Rehmani, Jinjun Chen

Publications

Modern cyber physical systems (CPSs) has widely being used in our daily lives because of development of information and communication technologies (ICT).With the provision of CPSs, the security and privacy threats associated to these systems are also increasing. Passive attacks are being used by intruders to get access to private information of CPSs. In order to make CPSs data more secure, certain privacy preservation strategies such as encryption, and k-anonymity have been presented in the past. However, with the advances in CPSs architecture, these techniques also need certain modifications. Meanwhile, differential privacy emerged as an efficient technique to protect CPSs …

Mobile Identity, Credential, And Access Management Framework, Peggy Renee Carnley

Masters Theses & Doctoral Dissertations

Organizations today gather unprecedented quantities of data from their operations. This data is coming from transactions made by a person or from a connected system/application. From personal devices to industry including government, the internet has become the primary means of modern communication, further increasing the need for a method to track and secure these devices. Protecting the integrity of connected devices collecting data is critical to ensure the trustworthiness of the system. An organization must not only know the identity of the users on their networks and have the capability of tracing the actions performed by a user but they …

Algorithm Selection Framework: A Holistic Approach To The Algorithm Selection Problem, Marc W. Chalé

Theses and Dissertations

A holistic approach to the algorithm selection problem is presented. The “algorithm selection framework" uses a combination of user input and meta-data to streamline the algorithm selection for any data analysis task. The framework removes the conjecture of the common trial and error strategy and generates a preference ranked list of recommended analysis techniques. The framework is performed on nine analysis problems. Each of the recommended analysis techniques are implemented on the corresponding data sets. Algorithm performance is assessed using the primary metric of recall and the secondary metric of run time. In six of the problems, the recall of …

An Analysis Of Learning Curve Theory & Diminishing Rates Of Learning, Dakotah W. Hogan

Theses and Dissertations

Traditional learning curve theory assumes a constant learning rate regardless of the number of units produced; however, a collection of theoretical and empirical evidence indicates that learning rates decrease as more units are produced in some cases. These diminishing learning rates cause traditional learning curves to underestimate required resources, potentially resulting in cost overruns. A diminishing learning rate model, Boones Learning Curve (2018), was recently developed to model this phenomenon. This research confirmed that Boones Learning Curve is more accurate in modeling observed learning curves using production data of 169 Department of Defense end-items. However, further empirical analysis revealed deficiencies …

Deep Learning In Clinical Natural Language Processing: A Methodical Review, Stephen Wu, Kirk Roberts, Surabhi Datta, Jingcheng Du, Zongcheng Ji, Yuqi Si, Sarvesh Soni, Qiong Wang, Qiang Wei, Yang Xiang, Bo Zhao, Hua Xu

Faculty, Staff and Student Publications

OBJECTIVE: This article methodically reviews the literature on deep learning (DL) for natural language processing (NLP) in the clinical domain, providing quantitative analysis to answer 3 research questions concerning methods, scope, and context of current research.

MATERIALS AND METHODS: We searched MEDLINE, EMBASE, Scopus, the Association for Computing Machinery Digital Library, and the Association for Computational Linguistics Anthology for articles using DL-based approaches to NLP problems in electronic health records. After screening 1,737 articles, we collected data on 25 variables across 212 papers.

RESULTS: DL in clinical NLP publications more than doubled each year, through 2018. Recurrent neural networks (60.8%) …

Using A Data Science Driven Approach To Analyzing Chemistry Hazard Code Data, Devlon Bomer

Theses and Dissertations

There exists a mostly unexplored ‘big data’ dataset comprising of chemical hazard and safety codes. The purpose of this research is to apply a data science methodology to the problem of exploring this data. The data was run through extract-transform-load protocols, and then through data mining algorithms. The results are described and discussed. More work could be done on the subject, but this paper starts the process.

Rock Mass Slope Stability Analysis Based On Terrestrial Lidar Survey On Selected Limestone Hills In Kinta Valley, Perak, Mohd Hellmy Muhammad Afiq Ariff

Student Works (2020-2029)

The use modern mapping technology is necessary in assessing slopes and cliffs, especially in tropical countries as it is mostly inaccessible and covered with thick vegetation which restricts the conventional data collection only at the base of the cliff. Overhanging and sub-vertical characteristics of limestone hills in Kinta Valley together with highly fractured and day-lighting joints increase the possibility of rock slope failure. The main objective of this research is to assess the stability and rock mass properties of the limestone hills in Kinta Valley based on the output provided by terrestrial LiDAR and scanline survey. Terrestrial laser scanning (TLS) …

Sentiment Analysis For Airline Services On Twitter Using Deep Learning With Word Embedding, Nour El Daim El Khalifa Mawada Mohamed

Student Works (2020-2029)

The use of social media platform in the airline industries have increased rapidly to allow analysis introduce the quality and performance of the services. The role of Sentiment Analysis (SA) is to classify people's opinions into different categories, such as positive and negative from text, using existing algorithms. However, existing approaches such as the Bag of Words (BOW) model is frequently used for text classification, where a document is mapped to a feature vector before the construction of the actual model, using machine learning techniques, like Logistical Regression and Support Vector algorithms. This problem has led to low accuracy in …

A Novel Item-Allocation Procedure For The Three-Form Planned Missing Data Design, Kyle M. Lang, E. Whitney G. Moore, Elizabeth M. Grandfield

Kinesiology, Health and Sport Studies

We propose a new method of constructing questionnaire forms in the three-form planned missing data design (PMDD). The random item allocation (RIA) procedure that we propose promises to dramatically simplify the process of implementing three-form PMDDs without compromising statistical performance. Our method is a stochastic approximation to the currently recommended approach of deterministically spreading a scale's items across the X-, A-, B-, and C-blocks when allocating the items in a three-form design. Direct empirical support for the performance of our method is only available for scales containing at least 12 items, so we also propose a modified approach for use …

Finding Common Ground For Citizen Empowerment In The Smart City, John D. Kelleher, Aphra Kerr

Articles

Corporate smart city initiatives are just one example of the contemporary culture of surveillance. They rely on extensive information gathering systems and Big Data analysis to predict citizen behaviour and optimise city services. In this paper we argue that many smart city and social media technologies result in a paradox whereby digital inclusion for the purposes of service provision also results in marginalisation and disempowerment of citizens. Drawing upon insights garnered from a digital inclusion workshop conducted in the Galapagos islands, we propose that critically and creatively unpacking the computational techniques embedded in data services is needed as a first …

A Systematic Literature Survey Of Unmanned Aerial Vehicle Based Structural Health Monitoring, Sreehari Sreenath

Theses, Dissertations and Capstones

Unmanned Aerial Vehicles (UAVs) are being employed in a multitude of civil applications owing to their ease of use, low maintenance, affordability, high-mobility, and ability to hover. UAVs are being utilized for real-time monitoring of road traffic, providing wireless coverage, remote sensing, search and rescue operations, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection. They are the next big revolution in technology and civil infrastructure, and it is expected to dominate more than $45 billion market value. The thesis surveys the UAV assisted Structural Health Monitoring or SHM literature over the last decade and categorize UAVs …

Critical Media, Information, And Digital Literacy: Increasing Understanding Of Machine Learning Through An Interdisciplinary Undergraduate Course, Barbara R. Burke, Elena Machkasova

Communication, Media, and Rhetoric Publications

Widespread use of Artificial Intelligence in all areas of today’s society creates a unique problem: algorithms used in decision-making are generally not understandable to those without a background in data science. Thus, those who use out-of-the-box Machine Learning (ML) approaches in their work and those affected by these approaches are often not in a position to analyse their outcomes and applicability. Our paper describes and evaluates our undergraduate course at the University of Minnesota Morris, which fosters understanding of the main ideas behind ML. With Communication, Media & Rhetoric and Computer Science faculty expertise, students from a variety of majors, …

Tufts Analytics Without Borders 2020 Conference Conference Summary Report, Tufts University

Analytics Without Borders

This conference provided a single venue for interdisciplinary research, collaboration, and communication on applied data analytics in the fields of business, public health, nutrition, and the environment. We advanced a shared agenda to harness the power of data sciences to discuss the complexities of health and nutrition problems through the lens of data sciences. The conference showcased numerous opportunities for students including hands-on workshops on topics related to data sciences, panel discussions on topics ranging from business to health, and both oral and poster presentations for sharing research.

Fusion-Net: Integration Of Dimension Reduction And Deep Learning Neural Network For Image Classification, Mohammad Masum, Philippe Laval

Published and Grey Literature from PhD Candidates

Building a deep network using original digital images requires learning many parameters which may reduce the accuracy rates. The images can be compressed by using dimension reduction methods and extracted reduced features can be feeding into a deep network for classification. Hence, in the training phase of the network, the number of parameters will be decreased. Principal Component Analysis is a well-known dimension reduction technique that leverage orthogonal linear transformation of the original data. In this paper, we propose a neural network-based framework, named Fusion-Net, which implements PCA on an image dataset (CIFAR-10) and then a neural network applies on …

Data Rescue & Curation Best Practices Guide, Ocul Data Community (Odc) Data Rescue Group

Western Libraries Publications

he aim of the Data Rescue & Curation Best Practices Guide is to provide an accessible and hands-on approach to handling data rescue and digital curation of at-risk data for use in secondary research. We provide a set of examples and workflows for addressing common challenges with social science survey data that can be applied to other social and behavioural research data. The goal of this guide and set of workflows presented is to improve librarians’ and data curators’ skills in providing access to high-quality, well-documented, and reusable research data. The aspects of data curation that are addressed throughout this …

Edge-Cloud Computing For Iot Data Analytics: Embedding Intelligence In The Edge With Deep Learning, Ananda Mohon M. Ghosh, Katarina Grolinger

Electrical and Computer Engineering Publications

Rapid growth in numbers of connected devices including sensors, mobile, wearable, and other Internet of Things (IoT) devices, is creating an explosion of data that are moving across the network. To carry out machine learning (ML), IoT data are typically transferred to the cloud or another centralized system for storage and processing; however, this causes latencies and increases network traffic. Edge computing has the potential to remedy those issues by moving computation closer to the network edge and data sources. On the other hand, edge computing is limited in terms of computational power and thus is not well suited for …