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Mapping Renewal: How An Unexpected Interdisciplinary Collaboration Transformed A Digital Humanities Project, Elise Tanner, Geoffrey Joseph 2020 UA Little Rock Center for Arkansas History and Culture

Mapping Renewal: How An Unexpected Interdisciplinary Collaboration Transformed A Digital Humanities Project, Elise Tanner, Geoffrey Joseph

Digital Initiatives Symposium

Funded by a National Endowment for Humanities (NEH) Humanities Collections and Reference Resources Foundations Grant, the UA Little Rock Center for Arkansas History and Culture’s “Mapping Renewal” pilot project focused on creating access to and providing spatial context to archival materials related to racial segregation and urban renewal in the city of Little Rock, Arkansas, from 1954-1989. An unplanned interdisciplinary collaboration with the UA Little Rock Arkansas Economic Development Institute (AEDI) has proven to be an invaluable partnership. One team member from each department will demonstrate the Mapping Renewal website and discuss how the collaborative process has changed and ...


Deepmag+ : Sniffing Mobile Apps In Magnetic Field Through Deep Learning, Rui Ning, Cong Wang, ChunSheng Xin, Jiang Li, Hongyi Wu 2020 Old Dominion University

Deepmag+ : Sniffing Mobile Apps In Magnetic Field Through Deep Learning, Rui Ning, Cong Wang, Chunsheng Xin, Jiang Li, Hongyi Wu

Electrical & Computer Engineering Faculty Publications

This paper reports a new side-channel attack to smartphones using the unrestricted magnetic sensor data. We demonstrate that attackers can effectively infer the Apps being used on a smartphone with an accuracy of over 80%, through training a deep Convolutional Neural Networks (CNN). Various signal processing strategies have been studied for feature extractions, including a tempogram based scheme. Moreover, by further exploiting the unrestricted motion sensor to cluster magnetometer data, the sniffing accuracy can increase to as high as 98%. To mitigate such attacks, we propose a noise injection scheme that can effectively reduce the App sniffing accuracy to only ...


Strategies For Information Technology Employee Retention, Stephen Horton 2020 Walden University

Strategies For Information Technology Employee Retention, Stephen Horton

Walden Dissertations and Doctoral Studies

Information technology (IT) employee retention is essential to IT departments tasked with supporting the goals and objectives of the organization. IT employees manage, support, and direct IT to drive business, pursue innovation, and create a competitive edge. The purpose of this qualitative exploratory multiple case study was to identify strategies that IT managers use to retain IT employees in order to support the goals and objectives of the IT organization. The population for this study consisted of 5 IT managers in the transportation industry. The IT managers selected for this study had subordinates and delegation duties and worked for employers ...


Factors Influencing Cloud Computing Adoption By Small Firms In The Payment Card Industry, Marie Njanje Tambe 2020 Walden University

Factors Influencing Cloud Computing Adoption By Small Firms In The Payment Card Industry, Marie Njanje Tambe

Walden Dissertations and Doctoral Studies

Technology acceptance is increasingly gaining attention in research considering the continuous exploits of innovation and various derived advantages. Cloud computing (CC) has shown to be the ideal solution for aligning information technology with business strategies. However, small to medium-sized enterprises (SMEs) in the payment card industry are reluctantly adopting this technology despite the benefits. This correlational study aims at investigating whether security, cost effectiveness, or regulatory compliance influence CC adoption by U.S. SMEs in the payment card sector. The study builds on the technology-organization-environment (TOE) framework and uses a previously validated instrument to assess CC adoption by decision-makers in ...


Search Results: Predicting Ranking Algorithms With User Ratings And User-Driven Data, Gary Michael Taylor 2020 Walden University

Search Results: Predicting Ranking Algorithms With User Ratings And User-Driven Data, Gary Michael Taylor

Walden Dissertations and Doctoral Studies

The purpose of this correlational quantitative study was to examine the possible relationship between user-driven parameters, user ratings, and ranking algorithms. The study’s population consisted of students and faculty in the information technology (IT) field at a university in Huntington, WV. Arrow’s impossibility theorem was used as the theoretical framework for this study. Complete survey data were collected from 47 students and faculty members in the IT field, and a multiple regression analysis was used to measure the correlations between the variables. The model was able to explain 85% of the total variability in the ranking algorithm. The ...


Exploring Strategies For Recruiting And Retaining Diverse Cybersecurity Professionals, Vivian Lyon 2020 Walden University

Exploring Strategies For Recruiting And Retaining Diverse Cybersecurity Professionals, Vivian Lyon

Walden Dissertations and Doctoral Studies

The cyber threat landscape has led some cybersecurity leaders to focus on a holistic approach encompassing people, processes, and technology to make their government agencies and organizations more responsive to a more diverse and inclusive cyber workforce to protect critical infrastructure from hackers or cybercriminals intent on causing harm. This qualitative multiple case study used Schein’s organizational culture theory to explore strategies used by cybersecurity leaders to attract, recruit, and retain diverse cybersecurity professionals to effectively and efficiently protect sensitive systems from rising cyber threats. The study's population consisted of cybersecurity leaders from 3 government agencies and 9 ...


Strategies For The Development Of It Disaster Recovery Plans In The Manufacturing Industry, Michael Landry Sartwell 2020 Walden University

Strategies For The Development Of It Disaster Recovery Plans In The Manufacturing Industry, Michael Landry Sartwell

Walden Dissertations and Doctoral Studies

Information technology (IT) leaders have reported technology disruptions because of natural disasters, terror attacks, or adversarial threats. Information technology leaders are concerned with technology disruptions, as these disruptions are costing organizations as much as $22,000 per minute. Grounded in Zachman’s framework, the purpose of this qualitative multiple case study was to explore strategies IT managers in the manufacturing industry use to develop IT disaster recovery (DR) plans to support business operations. The participants included 3 manufacturing IT professionals, 2 Department of Defense manufacturing infrastructure specialists, and 1outsourcing contractor, each from firms located in the central United States who ...


Early Detection Of Fake News On Social Media, Yang Liu 2019 New Jersey Institute of Technology

Early Detection Of Fake News On Social Media, Yang Liu

Dissertations

The ever-increasing popularity and convenience of social media enable the rapid widespread of fake news, which can cause a series of negative impacts both on individuals and society. Early detection of fake news is essential to minimize its social harm. Existing machine learning approaches are incapable of detecting a fake news story soon after it starts to spread, because they require certain amounts of data to reach decent effectiveness which take time to accumulate. To solve this problem, this research first analyzes and finds that, on social media, the user characteristics of fake news spreaders distribute significantly differently from those ...


A Qualitative Representation Of Spatial Scenes In R2 With Regions And Lines, Joshua Lewis 2019 University of Maine

A Qualitative Representation Of Spatial Scenes In R2 With Regions And Lines, Joshua Lewis

Electronic Theses and Dissertations

Regions and lines are common geographic abstractions for geographic objects. Collections of regions, lines, and other representations of spatial objects form a spatial scene, along with their relations. For instance, the states of Maine and New Hampshire can be represented by a pair of regions and related based on their topological properties. These two states are adjacent (i.e., they meet along their shared boundary), whereas Maine and Florida are not adjacent (i.e., they are disjoint).

A detailed model for qualitatively describing spatial scenes should capture the essential properties of a configuration such that a description of the represented ...


Information Extraction From Biomedical Text Using Machine Learning, Deepti Garg 2019 San Jose State University

Information Extraction From Biomedical Text Using Machine Learning, Deepti Garg

Master's Projects

Inadequate drug experimental data and the use of unlicensed drugs may cause adverse drug reactions, especially in pediatric populations. Every year the U.S. Food and Drug Administration approves human prescription drugs for marketing. The labels associated with these drugs include information about clinical trials and drug response in pediatric population. In order for doctors to make an informed decision about the safety and effectiveness of these drugs for children, there is a need to analyze complex and often unstructured drug labels. In this work, first, an exploratory analysis of drug labels using a Natural Language Processing pipeline is performed ...


Evaluating Data Quality Of Newborn Hearing Screening, Maria C. Sanchez Gomez, Kelly Dundon, Xidong Deng 2019 Carter Consulting Inc

Evaluating Data Quality Of Newborn Hearing Screening, Maria C. Sanchez Gomez, Kelly Dundon, Xidong Deng

Journal of Early Hearing Detection and Intervention

Scope

Jurisdictional-based Early Hearing Detection and Intervention Information Systems (EHDI-IS) collect data on the hearing screening and follow-up status of infants across the United States. These systems serve as tools that assist EHDI programs’ staff and partners in their tracking activities and provide a variety of data reports to help ensure that all children who are deaf/hard of hearing (D/HH) are identified early and receive recommended intervention services. The quality and timeliness of the data collected with these systems are crucial to effectively meeting these goals.

Methodology

Forty-eight EHDI programs, funded by the Centers for Disease Control and ...


Music Retrieval System Using Query-By-Humming, Parth Patel 2019 San Jose State University

Music Retrieval System Using Query-By-Humming, Parth Patel

Master's Projects

Music Information Retrieval (MIR) is a particular research area of great interest because there are various strategies to retrieve music. To retrieve music, it is important to find a similarity between the input query and the matching music. Several solutions have been proposed that are currently being used in the application domain(s) such as Query- by-Example (QBE) which takes a sample of an audio recording playing in the background and retrieves the result. However, there is no efficient approach to solve this problem in a Query-by-Humming (QBH) application. In a Query-by-Humming application, the aim is to retrieve music that ...


A Hybrid Approach For Multi-Document Text Summarization, Rashmi Varma 2019 San Jose State University

A Hybrid Approach For Multi-Document Text Summarization, Rashmi Varma

Master's Projects

Text summarization has been a long studied topic in the field of natural language processing. There have been various approaches for both extractive text summarization as well as abstractive text summarization. Summarizing texts for a single document is a methodical task. But summarizing multiple documents poses as a greater challenge. This thesis explores the application of Latent Semantic Analysis, Text-Rank, Lex-Rank and Reduction algorithms for single document text summarization and compares it with the proposed approach of creating a hybrid system combining each of the above algorithms, individually, with Restricted Boltzmann Machines for multi-document text summarization and analyzing how all ...


Hybrid Recommender Systems Via Spectral Learning And A Random Forest, Alyssa Williams 2019 East Tennessee State University

Hybrid Recommender Systems Via Spectral Learning And A Random Forest, Alyssa Williams

Electronic Theses and Dissertations

We demonstrate spectral learning can be combined with a random forest classifier to produce a hybrid recommender system capable of incorporating meta information. Spectral learning is supervised learning in which data is in the form of one or more networks. Responses are predicted from features obtained from the eigenvector decomposition of matrix representations of the networks. Spectral learning is based on the highest weight eigenvectors of natural Markov chain representations. A random forest is an ensemble technique for supervised learning whose internal predictive model can be interpreted as a nearest neighbor network. A hybrid recommender can be constructed by first ...


Extracting Social Network From Literary Prose, Tarana Tasmin Bipasha 2019 University of Arkansas, Fayetteville

Extracting Social Network From Literary Prose, Tarana Tasmin Bipasha

Theses and Dissertations

This thesis develops an approach to extract social networks from literary prose, namely, Jane Austen’s published novels from eighteenth- and nineteenth- century. Dialogue interaction plays a key role while we derive the networks, thus our technique relies upon our ability to determine when two characters are in conversation. Our process involves encoding plain literary text into the Text Encoding Initiative’s (TEI) XML format, character name identification, conversation and co-occurrence detection, and social network construction. Previous work in social network construction for literature have focused on drama, specifically manually TEI-encoded Shakespearean plays in which character interactions are much easier ...


Sentiment Analysis, Quantification, And Shift Detection, Kevin Labille 2019 University of Arkansas, Fayetteville

Sentiment Analysis, Quantification, And Shift Detection, Kevin Labille

Theses and Dissertations

This dissertation focuses on event detection within streams of Tweets based on sentiment quantification. Sentiment quantification extends sentiment analysis, the analysis of the sentiment of individual documents, to analyze the sentiment of an aggregated collection of documents. Although the former has been widely researched, the latter has drawn less attention but offers greater potential to enhance current business intelligence systems. Indeed, knowing the proportion of positive and negative Tweets is much more valuable than knowing which individual Tweets are positive or negative. We also extend our sentiment quantification research to analyze the evolution of sentiment over time to automatically detect ...


A Transformative Concept: From Data Being Passive Objects To Data Being Active Subjects, Hans-Peter Plag, Shelley-Ann Jules-Plag 2019 Old Dominion University

A Transformative Concept: From Data Being Passive Objects To Data Being Active Subjects, Hans-Peter Plag, Shelley-Ann Jules-Plag

OEAS Faculty Publications

The exploitation of potential societal benefits of Earth observations is hampered by users having to engage in often tedious processes to discover data and extract information and knowledge. A concept is introduced for a transition from the current perception of data as passive objects (DPO) to a new perception of data as active subjects (DAS). This transition would greatly increase data usage and exploitation, and support the extraction of knowledge from data products. Enabling the data subjects to actively reach out to potential users would revolutionize data dissemination and sharing and facilitate collaboration in user communities. The three core elements ...


Iomt Malware Detection Approaches: Analysis And Research Challenges, Mohammad Wazid, Ashok Kumar Das, Joel J.P.C. Rodrigues, Sachin Shetty, Youngho Park 2019 Old Dominion University

Iomt Malware Detection Approaches: Analysis And Research Challenges, Mohammad Wazid, Ashok Kumar Das, Joel J.P.C. Rodrigues, Sachin Shetty, Youngho Park

VMASC Publications

The advancement in Information and Communications Technology (ICT) has changed the entire paradigm of computing. Because of such advancement, we have new types of computing and communication environments, for example, Internet of Things (IoT) that is a collection of smart IoT devices. The Internet of Medical Things (IoMT) is a specific type of IoT communication environment which deals with communication through the smart healthcare (medical) devices. Though IoT communication environment facilitates and supports our day-to-day activities, but at the same time it has also certain drawbacks as it suffers from several security and privacy issues, such as replay, man-in-the-middle, impersonation ...


Optimal Design And Ownership Structures Of Innovative Retail Payment Systems, Zhiling GUO, Dan MA 2019 Singapore Management University

Optimal Design And Ownership Structures Of Innovative Retail Payment Systems, Zhiling Guo, Dan Ma

Research Collection School Of Information Systems

In response to the Fintech trend, an ongoing debate in the banking industry is how to design the new-generation interbank retail payment and settlement system. We propose a two-stage analytical model that takes into account the value-risk tradeoff in the new payment system design, as well as banks’ participation incentives and adoption timing decisions. We find that, as the system base value increases, banks tend to synchronize their investment and adoption decisions. When the system base value is low and banks are heterogeneous, bank association ownership maximizes social welfare. When both the system base value and bank heterogeneity are moderate ...


Study Group Travel Behaviour Patterns From Large-Scale Smart Card Data, Xiancai TIAN, Baihua ZHENG 2019 Singapore Management University

Study Group Travel Behaviour Patterns From Large-Scale Smart Card Data, Xiancai Tian, Baihua Zheng

Research Collection School Of Information Systems

In this paper, we aim at studying the group travel behaviour (GTB) patterns from large-scale auto fare collection (AFC) data. GTB is defined as two or more commuters intentionally and regularly traveling together from an origin to a destination. We propose a method to identify GTB accurately and efficiently and apply our method to the Singapore AFC dataset to reveal the GTB patterns of Singapore commuters. The case study proves that our method is able to identify GTB patterns more accurately and efficiently than the state-of-the-art.


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