Other Computer Sciences Commons^™

Synergistic Use Of Remote Sensing And Modeling For Estimating Net Primary Productivity In The Red Sea With Vgpm, Eppley-Vgpm, And Cbpm Models Intercomparison, Wenzhao Li, Surya Prakash Tiwari, Hesham El-Askary, Mohamed Ali Qurban, Vassilis Amiridis, K. P. Manikandan, Michael J. Garay, Olga V. Kalashnikova, Thomas C. Piechota, Daniele C. Struppa

Mathematics, Physics, and Computer Science Faculty Articles and Research

Primary productivity (PP) has been recently investigated using remote sensing-based models over quite limited geographical areas of the Red Sea. This work sheds light on how phytoplankton and primary production would react to the effects of global warming in the extreme environment of the Red Sea and, hence, illuminates how similar regions may behave in the context of climate variability. study focuses on using satellite observations to conduct an intercomparison of three net primary production (NPP) models--the vertically generalized production model (VGPM), the Eppley-VGPM, and the carbon-based production model (CbPM)--produced over the Red Sea domain for the 1998-2018 time period. …

Ml-Medic: A Preliminary Study Of An Interactive Visual Analysis Tool Facilitating Clinical Applications Of Machine Learning For Precision Medicine, Laura Stevens, David Kao, Jennifer Hall, Carsten Görg, Kaitlyn Abdo, Erik Linstead

Engineering Faculty Articles and Research

Accessible interactive tools that integrate machine learning methods with clinical research and reduce the programming experience required are needed to move science forward. Here, we present Machine Learning for Medical Exploration and Data-Inspired Care (ML-MEDIC), a point-and-click, interactive tool with a visual interface for facilitating machine learning and statistical analyses in clinical research. We deployed ML-MEDIC in the American Heart Association (AHA) Precision Medicine Platform to provide secure internet access and facilitate collaboration. ML-MEDIC’s efficacy for facilitating the adoption of machine learning was evaluated through two case studies in collaboration with clinical domain experts. A domain expert review was also …

A Pilot Study On The Impact Of Teaching Assistant Led Cs1 Study Sessions Using Peer Instruction, Megan E. Gilbert

Senior Honors Projects, 2020-current

James Madison University’s Computer Science program strives to be a student-centered learning environment with a focus on creating a community for undergraduate success. National data reveals computer science has the lowest student retention rate compared to other STEM majors. The National Center for Women and Information Technology (NCWIT) has compiled a list of ways to retain students in Computer Science. In particular, NCWIT calls for collaboration indicate that “a sense of belonging, or a feeling of fit, is important for supporting student interest and persistence.” One aspect of creating community is the department’s longstanding commitment to provide undergraduate teaching assistants …

The Use Of Digital Millenium Copyright Act To Stifle Speech Through Non-Copyright Related Takedowns, Miller Freeman

Seattle Journal of Technology, Environmental, & Innovation Law

In 1998, Congress passed the Digital Millennium Copyright Act. This law provided new methods of protecting copyright in online media. These protections shift the normal judicial process that would stop the publication of infringing materials to private actors: the online platforms. As a result, online platforms receive notices of infringement and issue takedowns of allegedly copyrighted works without the judicial process which normally considers the purpose of the original notice of infringement. In at least one case, discussed in detail below, this has resulted in a notice and takedown against an individual for reasons not related to the purpose of …

An Exploration Of Methods For Classifying Air-Written Letters From The Spanish Alphabet, Manuel Serna-Aguilera

Computer Science and Computer Engineering Undergraduate Honors Theses

The ability to recognize human activity, especially air-writing, is an interesting challenge as one could identify any letter from many languages. I intend to investigate this problem of air-writing, but with the added twist of including the following letters from the Spanish alphabet: Á, É, Í, Ó, Ú, Ü, and Ñ. With this new alphabet, I set out to see what kinds of classifiers work best and on what kinds of data, since letters can be represented in multiple ways.

My tracking system will consist of a regular camera and a subject who will draw with a brightly colored marker …

Integrated Machine Learning And Bioinformatics Approaches For Prediction Of Cancer-Driving Gene Mutations, Oluyemi Odeyemi

Computational and Data Sciences (PhD) Dissertations

Cancer arises from the accumulation of somatic mutations and genetic alterations in cell division checkpoints and apoptosis, this often leads to abnormal tumor proliferation. Proper classification of cancer-linked driver mutations will considerably help our understanding of the molecular dynamics of cancer. In this study, we compared several cancer-specific predictive models for prediction of driver mutations in cancer-linked genes that were validated on canonical data sets of functionally validated mutations and applied to a raw cancer genomics data. By analyzing pathogenicity prediction and conservation scores, we have shown that evolutionary conservation scores play a pivotal role in the classification of cancer …

Faculty Perceptions Of Open Educational Resources In Cyber Curriculum: A Pilot Study, Alan Stines

Masters Theses & Doctoral Dissertations

The cyber landscape is growing and evolving at a fast pace. Public and private industries need qualified applicants to protect and defend information systems that drive the digital economy. Currently, there are not enough candidates in the pipeline to fill this need in the workforce. The digital economy is still growing, thus presenting an even greater need for skilled workers in the future. The lack of a strong workforce in cybersecurity presents many challenges to safeguarding U.S. national security and citizens across the world. The William and Flora Hewlett Foundation defines Open Educational Resources (OER) as teaching, learning, and research …

Applications Of Digital Remote Sensing To Quantify Glacier Change In Glacier And Mount Rainier National Parks, Brianna Clark

Electronic Theses and Dissertations

Digital remote sensing and geographic information systems were employed in performing area and volume calculations on glacial landscapes. Characteristics of glaciers from two geographic regions, the Intermountain Region (between the Rocky Mountain and Cascade Ranges) and the Pacific Northwest, were estimated for the years 1985, 2000, and 2015. Glacier National Park was studied for the Intermountain Region whereas Mount Rainier National Park was representative of the glaciers in the Pacific Northwest. Within the thirty year period of the study, the glaciers in Glacier National Park decreased in area by 27.5 percent while those on Mount Rainier only decreased by 5.7 …

Interactions In Visualizations To Support Knowledge Activation, Kari Sandouka

Masters Theses & Doctoral Dissertations

Humans have several exceptional abilities, one of which is the perceptual tasks of their visual sense. Humans have the unique ability to perceive data and identify patterns, trends, and outliers. This research investigates the design of interactive visualizations to identify the benefits of interacting with information. The research question leading the investigation is how does interacting with visualizations support analytical reasoning of emergent information to activate knowledge? The study uses the theory of distributed cognition and human-information interaction to apply the design science research framework. The motivation behind the research is to identify guidelines for interactive visualizations to enhance a …

Vzwam Web-Based Lookup, Ruben Claudio

Masters Theses & Doctoral Dissertations

This web-based lookup will allow V employees to find territory sales rep much faster. It will simplify the process and eliminate manual processes.

At the moment, a combination of multiple manual processes is needed to find territory sales reps. The company’s CRM does not allow to find rep sales quickly. When an in-house sales representative is talking to a prospect, this sales rep has to go through a few series of steps to find an outside or territory sales rep --which is usually needed to schedule in-person meetings, that results in delays while doing transactions with the prospects. Besides, because …

Improving Scientist Productivity, Architecture Portability, And Performance In Parflow, Michael Burke

Boise State University Theses and Dissertations

Legacy scientific applications represent significant investments by universities, engineers, and researchers and contain valuable implementations of key scientific computations. Over time hardware architectures have changed. Adapting existing code to new architectures is time consuming, expensive, and increases code complexity. The increase in complexity negatively affects the scientific impact of the applications. There is an immediate need to reduce complexity. We propose using abstractions to manage and reduce code complexity, improving scientific impact of applications.

This thesis presents a set of abstractions targeting boundary conditions in iterative solvers. Many scientific applications represent physical phenomena as a set of partial differential equations …

Novel Inference Methods For Generalized Linear Models Using Shrinkage Priors And Data Augmentation., Arinjita Bhattacharyya

Electronic Theses and Dissertations

Generalized linear models have broad applications in biostatistics and sociology. In a regression setup, the main target is to find a relevant set of predictors out of a large collection of covariates. Sparsity is the assumption that only a few of these covariates in a regression setup have a meaningful correlation with an outcome variate of interest. Sparsity is incorporated by regularizing the irrelevant slopes towards zero without changing the relevant predictors and keeping the resulting inferences intact. Frequentist variable selection and sparsity are addressed by popular techniques like Lasso, Elastic Net. Bayesian penalized regression can tackle the curse of …

Knot Flow Classification And Its Applications In Vehicular Ad-Hoc Networks (Vanet), David Schmidt

Electronic Theses and Dissertations

Intrusion detection systems (IDSs) play a crucial role in the identification and mitigation for attacks on host systems. Of these systems, vehicular ad hoc networks (VANETs) are difficult to protect due to the dynamic nature of their clients and their necessity for constant interaction with their respective cyber-physical systems. Currently, there is a need for a VANET-specific IDS that meets this criterion. To this end, a spline-based intrusion detection system has been pioneered as a solution. By combining clustering with spline-based general linear model classification, this knot flow classification method (KFC) allows for robust intrusion detection to occur. Due its …

A Model For The Spread Of Infectious Diseases In A Region, Elizabeth Hunter, Brian Mac Namee, John D. Kelleher

Articles

In understanding the dynamics of the spread of an infectious disease, it is important to understand how a town’s place in a network of towns within a region will impact how the disease spreads to that town and from that town. In this article, we take a model for the spread of an infectious disease in a single town and scale it up to simulate a region containing multiple towns. The model is validated by looking at how adding additional towns and commuters influences the outbreak in a single town. We then look at how the centrality of a town …

A Vertical Cooperation Model To Manage Digital Collections And Institutional Resources, Jack M. Maness, Kim Pham, Fernando Reyes, Jeff Rynhart

University Libraries: Faculty Scholarship

The technology space of the University of Denver Libraries to manage digital collections and institutional resources isn’t relegated to one department on campus – rather, it distributed across a network of collaborators with the skills and expertise to provide that support. The infrastructure, which is comprised of an archival metadata management system (Archivespace), a digital repository (Node.js + ElasticSearch), preservation storage (ArchivesDirect), and a streaming server (Kaltura) is independently but cooperatively managed across IT, library departments and vendors. The coordinated eort of digital curation activities still allows each group to focus on the service they have the most vested interest …

Completing A Crowdsourcing Task Instead Of An Assignment; What Do University Students Think?, Javed-Vassilis Khan, Konstantinos Papangelis, Panos Markopoulos

Presentations and other scholarship

University educators actively seek realistic projects to include in their educational activities. However, finding an actually realistic project is not trivial. The rise of crowdsourcing platforms, in which a variety of tasks are offered in the form of an open call, might be an alternative source to help educators scaleup project-based learning. But how do university students feel about executing crowdsourcing tasks instead of their typical assignments? In a study with 24 industrial design students, we investigate students' attitudes on introducing crowdsourcing tasks as assignments. Based on our study we offer four suggestions to universities that consider integrating crowdsourcing tasks …

Smart Cities At Play: Lived Experiences, Emerging Forms Of Playfulness, And Problems Of Participation, Konstantinos Papangelis, Jin-Ha Lee, Michael Saker, Catherine Jones

Presentations and other scholarship

In recent years, the notion of smart cities has become the focus of a growing body of research. To date, much of this attention has revolved around the technical aspect, with related concerns including the creation and implementation of suitable smart city technologies. What is notably missing from these discussions, however, is a consideration of the lived experience of supposedly 'smart spaces' and the extent to which physical and digital environments are currently producing new forms of play and playfulness that can be contextualized within this field. With this in mind, the purpose of our workshop is as follows. First, …

Quantum Computing And Quantum Algorithms, Daniel Serban

Senior Honors Theses

The field of quantum computing and quantum algorithms is studied from the ground up. Qubits and their quantum-mechanical properties are discussed, followed by how they are transformed by quantum gates. From there, quantum algorithms are explored as well as the use of high-level quantum programming languages to implement them. One quantum algorithm is selected to be implemented in the Qiskit quantum programming language. The validity and success of the resulting computation is proven with matrix multiplication of the qubits and quantum gates involved.

Philosophical Perspectives, Jochen Albrecht

Publications and Research

This entry follows in the footsteps of Anselin’s famous 1989 NCGIA working paper entitled “What is special about spatial?” (a report that is very timely again in an age when non-spatial data scientists are ignorant of the special characteristics of spatial data), where he outlines three unrelated but fundamental characteristics of spatial data. In a similar vein, I am going to discuss some philosophical perspectives that are internally unrelated to each other and could warrant individual entries in this Body of Knowledge. The first one is the notions of space and time and how they have evolved in …

Fast Clustering Using A Grid-Based Underlying Density Function Approximation, Daniel Brown

Master of Science in Computer Science Theses

Clustering is an unsupervised machine learning task that seeks to partition a set of data into smaller groupings, referred to as “clusters”, where items within the same cluster are somehow alike, while differing from those in other clusters. There are many different algorithms for clustering, but many of them are overly complex and scale poorly with larger data sets. In this paper, a new algorithm for clustering is proposed to solve some of these issues. Density-based clustering algorithms use a concept called the “underlying density function”, which is a conceptual higher-dimension function that describes the possible results from the continuous …

Storage Management Strategy In Mobile Phones For Photo Crowdsensing, En Wang, Zhengdao Qu, Xinyao Liang, Xiangyu Meng, Yongjian Yang, Dawei Li, Weibin Meng

Department of Computer Science Faculty Scholarship and Creative Works

In mobile crowdsensing, some users jointly finish a sensing task through the sensors equipped in their intelligent terminals. In particular, the photo crowdsensing based on Mobile Edge Computing (MEC) collects pictures for some specific targets or events and uploads them to nearby edge servers, which leads to richer data content and more efficient data storage compared with the common mobile crowdsensing; hence, it has attracted an important amount of attention recently. However, the mobile users prefer uploading the photos through Wifi APs (PoIs) rather than cellular networks. Therefore, photos stored in mobile phones are exchanged among users, in order to …

Book Genre Classification By Its Cover Using A Multi-View Learning Approach, Chandra Shakhar Kundu

Masters Theses & Specialist Projects

An interesting topic in the visual analysis is to determine the genre of a book by its cover. The book cover is the very first communication to the reader which shapes the reader’s expectation about the type of the book. Each book cover is carefully designed by the cover designers and typographers to convey the visual representation of its content. In this study, we explore several different deep learning approaches for predicting the genre from the cover image alone, such as MobileNet V1, MobileNet V2, ResNet50, Inception V2. Moreover, we add an extra modality by extracting text from the cover …

Explainable Deep Learning For Medical Image Analysis, Brennan Rhoadarmer

UCARE: Research Products

Explainable Deep Learning for Medical Image Analysis is a project focused on improving the ability for deep learning models to explain the reasoning behind their classification in order to improve their viability in the medical field, where explanations of decisions is critical for the care of patients. In order to explore this topic, we work to implement GradCAM, which is a new method of determining the cause classification in models by tracing back through the model layers to the input.

An Examination Of The Work Practices Of Crowdfarms, Yihong Wang, Konstantinos Papangelis, Michael Saker, Ioanna Lykourentzou, Vassilis-Javed Khan, Alan Chamberlain, Jonathan Grudin

Presentations and other scholarship

Crowdsourcing is a new value creation business model. Annual revenue of the Chinese market alone is hundreds of millions of dollars, yet few studies have focused on the practices of the Chinese crowdsourcing workforce, and those that do mainly focus on solo crowdworkers. We have extended our study of solo crowdworker practices to include crowdfarms, a relatively new entry to the gig economy: small companies that carry out crowdwork as a key part of their business. We report here on interviews of people who work in53 crowdfarms. We describe how crowdfarms procure jobs, carry out macrotasks and microtasks, manage their …

The Influence Of Peer And Parental Norms On First-Generation College Students’ Binge Drinking Trajectories, Graham T. Diguiseppi, Jordan P. Davis, Matthew K. Meisel, Melissa A. Clark, Mya L. Roberson, Miles Q. Ott, Nancy P. Barnett

Statistical and Data Sciences: Faculty Publications

Introduction: First-generation college students are those whose parents have not completed a four-year college degree. The current study addressed the lack of research on first-generation college students’ alcohol use by comparing the binge drinking trajectories of first-generation and continuing-generation students over their first three semesters. The dynamic influence of peer and parental social norms on students’ binge drinking frequencies were also examined. Methods: 1342 college students (n = 225 first-generation) at one private University completed online surveys. Group differences were examined at Time 1, and latent growth-curve models tested the association between first-generation status and social norms (peer descriptive, peer …

Byod-Insure: A Security Assessment Model For Enterprise Byod, Melva Ratchford

Masters Theses & Doctoral Dissertations

As organizations continue allowing employees to use their personal mobile devices to access the organizations’ networks and the corporate data, a phenomenon called ‘Bring Your Own Device’ or BYOD, proper security controls need to be adopted not only to secure the corporate data but also to protect the organizations against possible litigation problems. Until recently, current literature and research have been focused on specific areas or solutions regarding BYOD. The information associated with BYOD security issues in the areas of Management, IT, Users and Mobile Device Solutions is fragmented. This research is based on a need to provide a holistic …

Multi-Label Model For Toxicity Prediction, Xiu Huan Yap, Michael L. Raymer

Celebration of Undergraduate & Graduate Research, Scholarship, and Creative Activities Materials

Most computational predictive models are specifically trained for a single toxicity endpoint. Since more than 1300 toxicity assays have been reported in the TOXCAST dashboard, achieving high coverage over this growing number of toxicity endpoints remains challenging. Furthermore, single-endpoint models lack the ability to learn dependencies between endpoints, such as those targeting similar biological pathways, which may be used to boost model performance. In this study, we characterize the performance of 3 multi-label classification (MLC) models, namely Classifier Chains (CC), Label Powersets (LP) and Stacking (SBR), on Tox21 challenge data. These MLC models employ the Problem Transformation approach, which is …

Digital Forensic Readiness: An Examination Of Law Enforcement Agencies In The State Of Maryland, James B. Mcnicholas Iii

Masters Theses & Doctoral Dissertations

Digital forensic readiness within the law enforcement community, especially at the local level, has gone mostly unexplored. As a result, a current lack of data exists that examines the digital forensic readiness of individual agencies, the possibility of proximity relationships, and correlations between readiness and backlogs. This quantitative, crosssectional research study sought to explore these issues by focusing on the state of Maryland. The study resulted in the creation of a digital forensic readiness scoring model that was then used to assign digital forensic readiness scores to thirty (30) of the one-hundred-forty-one (141) law enforcement agencies throughout Maryland. It was …

Smart Cities At Play: Technology And Emerging Forms Of Playfulness, Konstantinos Papangelis, Michael Saker, Catherine Jones

Articles

Editorial. No abstract is available.

The Adoption Of Cryptocurrency Technology Into The Us Banking Infrastructure, Trevor Melito

Senior Theses

This thesis examines the possibility of using Blockchain technology to permanently change the payment structure of the US banking system. First, I examine the current technology that dominates the banking sector. I introduce the most frequently used payments methods including Automatic Clearing House transfers and wire transfers, both domestically and internationally. In addition, I highlight the major players controlling these transactions. Under the current system, frictions between senders and receivers cause billions of dollars in losses each year.

Next, I examine Blockchain’s roots along with some similar cryptocurrency technology, namely Distributed Ledger Technology and Smart Contracts. The transparency, security, and …