Computer Sciences Commons^™

Toward Multi-Label Sentiment Analysis: A Transfer Learning Based Approach, Jie Tao, Xing Fang

Faculty Publications - Information Technology

Sentiment analysis is recognized as one of the most important sub-areas in Natural Language Processing (NLP) research, where understanding implicit or explicit sentiments expressed in social media contents is valuable to customers, business owners, and other stakeholders. Researchers have recognized that the generic sentiments extracted from the textual contents are inadequate, thus, Aspect Based Sentiment Analysis (ABSA) was coined to capture aspect sentiments expressed toward specific review aspects. Existing ABSA methods not only treat the analytical problem as single-label classification that requires a fairly large amount of labelled data for model training purposes, but also underestimate the entity aspects …

A Single-Source Shortest Path Algorithm For Dynamic Graphs, Muteb Alshammari, Abdelmounaam Rezgui

Faculty Publications - Information Technology

Graphs are mathematical structures used in many applications. In recent years, many applications emerged that require the processing of large dynamic graphs where the graph’s structure and properties change constantly over time. Examples include social networks, communication networks, transportation networks, etc. One of the most challenging problems in large scale dynamic graphs is the single-source shortest path (SSSP) problem. Traditional solutions (based on Dijkstra’s algorithms) to the SSSP problem do not scale to large dynamic graphs with a high change frequency. In this paper, we propose an efficient SSSP algorithm for large dynamic graphs. We first present our algorithm and …

Modulation Of Medical Condition Likelihood By Patient History Similarity, Jonathan Turner, Dympna O'Sullivan, Jon Bird

Articles

Introduction: We describe an analysis that modulates the simple population prevalence derived likelihood of a particular condition occurring in an individual by matching the individual with other individuals with similar clinical histories and determining the prevalence of the condition within the matched group.

Methods: We have taken clinical event codes and dates from anonymised longitudinal primary care records for 25,979 patients with 749,053 recorded clinical events. Using a nearest neighbour approach, for each patient, the likelihood of a condition occurring was adjusted from the population prevalence to the prevalence of the condition within those patients with the closest …

Opening Books And The National Corpus Of Graduate Research, William A. Ingram, Edward A. Fox, Jian Wu

Computer Science Faculty Publications

Virginia Tech University Libraries, in collaboration with Virginia Tech Department of Computer Science and Old Dominion University Department of Computer Science, request $505,214 in grant funding for a 3-year project, the goal of which is to bring computational access to book-length documents, demonstrating that with Electronic Theses and Dissertations (ETDs). The project is motivated by the following library and community needs. (1) Despite huge volumes of book-length documents in digital libraries, there is a lack of models offering effective and efficient computational access to these long documents. (2) Nationwide open access services for ETDs generally function at the metadata level. …

The Influence Of Cognitive Factors And Personality Traits On Mobile Device User's Information Security Behavior, Nils Lau

CCAC Theses and Dissertations

As individuals have become more dependent on mobile devices to communicate, to seek information, and to conduct business, their susceptibility to various threats to information security has also increased. Research has consistently shown that a user’s intention is a significant antecedent of information security behavior. Although research on user’s intention has expanded in the last few years, not enough is known about how cognitive factors and personality traits impact the adoption and use of mobile device security technologies.

The purpose of this research was to empirically investigate the influence of cognitive factors and personality traits on mobile device user’s intention …

An Investigation Of The Factors That Contribute To The Perceived Likelihood Of Compliance With The Hipaa Security Rule Among Healthcare Covered Entities And Business Associates, James Furstenberg

CCAC Theses and Dissertations

The Health Insurance Portability and Accountability Act (HIPAA) Security Rule (SR) mandate provides a national standard for the safeguard of electronically protected health information (ePHI). SR compliance enforcement efforts started in 2005; however, U.S.-based covered entities and business associates (CEs & BAs) remain challenged to comply with the HIPAA SR regulatory strategy. Although there is a significant volume of academic research on HIPAA compliance, research specific to the SR is sparse.

This study addressed the research gap by designing a unique conceptual model that assessed factors affecting CEs & BAs compliance (or non-compliance) with the SR regulatory strategy. The primary …

Prediction Of Sudden Cardiac Death Using Ensemble Classifiers, Ayman Momtaz El-Geneidy

CCAC Theses and Dissertations

Sudden Cardiac Death (SCD) is a medical problem that is responsible for over 300,000 deaths per year in the United States and millions worldwide. SCD is defined as death occurring from within one hour of the onset of acute symptoms, an unwitnessed death in the absence of pre-existing progressive circulatory failures or other causes of deaths, or death during attempted resuscitation. Sudden death due to cardiac reasons is a leading cause of death among Congestive Heart Failure (CHF) patients. The use of Electronic Medical Records (EMR) systems has made a wealth of medical data available for research and analysis. Supervised …

Examining The Creation Of Information Privacy Culture In Healthcare Organizations To Achieve Collective Hipaa Compliance Practice, Osborne Obeng

CCAC Theses and Dissertations

The digitization and transfer of patient records has increased the risk of patient data being improperly handled by healthcare organizations. In light of this growing concern, the United States government and state authorities have implemented various regulations to mitigate the privacy concerns. Beside privacy regulations, healthcare organizations have been forced by other pressures such as organization’s competitive pressures, resources, ethical responsibilities, and legitimacy to implement privacy safeguards. However, surveys show that healthcare organizations fail to achieve information privacy compliance. This study examined the creation of information privacy culture from the different occupational communities in healthcare organizations to help achieve information …

Improvements To Iterated Local Search For Microaggregation, Tracy Bierman

CCAC Theses and Dissertations

Microaggregation is a disclosure control method that uses k-anonymity to protect confidentiality in microdata while seeking minimal information loss. The problem is NP-hard. Iterated local search for microaggregation (ILSM) is an effective metaheuristic algorithm that consistently identifies better quality solutions than extant microaggregation methods. The present work presents improvements to local search, the perturbation operations and acceptance criterion within ILSM.

The first, ILSMC, targets changed clusters within local search (LS) to avoid vast numbers of comparison tests, significantly reducing execution times. Second, a new probability distribution yields a better perturbation operator for most cases, significantly reducing the number of iterations …

Identifying Physicians’ User Experience (Ux) Pain Points In Using Electronic Health Record (Ehr) Systems, Elizabeth Janelle Arceneaux

CCAC Theses and Dissertations

Healthcare institutions have migrated to online electronic documentation through the means of Electronic Health Record (EHR) systems. Physicians rely on these systems to support their various clinical work processes, such as entering clinical orders, reviewing essential clinical data, and making important medical decisions using reporting analytics. Although EHR systems appear to be useful and have known advantages over paper records, studies suggest there are persistent user interface design problems that may hinder physician productivity. The study focused on the research problem that EHR system designs create productivity problems for physician users who frequently report that system workflows are inefficient and …

Predictive Accuracy Of Recommender Algorithms, William Blake Noffsinger

CCAC Theses and Dissertations

Recommender systems present a customized list of items based upon user or item characteristics with the objective of reducing a large number of possible choices to a smaller ranked set most likely to appeal to the user. A variety of algorithms for recommender systems have been developed and refined including applications of deep learning neural networks. Recent research reports point to a need to perform carefully controlled experiments to gain insights about the relative accuracy of different recommender algorithms, because studies evaluating different methods have not used a common set of benchmark data sets, baseline models, and evaluation metrics. The …

Development Of Criteria For Mobile Device Cybersecurity Threat Classification And Communication Standards (Ctc&Cs), Emmanuel Jigo

CCAC Theses and Dissertations

The increasing use of mobile devices and the unfettered access to cyberspace has introduced new threats to users. Mobile device users are continually being targeted for cybersecurity threats via vectors such as public information sharing on social media, user surveillance (geolocation, camera, etc.), phishing, malware, spyware, trojans, and keyloggers. Users are often uninformed about the cybersecurity threats posed by mobile devices. Users are held responsible for the security of their device that includes taking precautions against cybersecurity threats. In recent years, financial institutions are passing the costs associated with fraud to the users because of the lack of security.

The …

Classifying Relations Using Recurrent Neural Network With Ontological-Concept Embedding, Mario J. Lorenzo

CCAC Theses and Dissertations

Relation extraction and classification represents a fundamental and challenging aspect of Natural Language Processing (NLP) research which depends on other tasks such as entity detection and word sense disambiguation. Traditional relation extraction methods based on pattern-matching using regular expressions grammars and lexico-syntactic pattern rules suffer from several drawbacks including the labor involved in handcrafting and maintaining large number of rules that are difficult to reuse. Current research has focused on using Neural Networks to help improve the accuracy of relation extraction tasks using a specific type of Recurrent Neural Network (RNN). A promising approach for relation classification uses an RNN …

Cybersecurity Risk-Responsibility Taxonomy: The Role Of Cybersecurity Social Responsibility In Small Enterprises On Risk Of Data Breach, Keiona Davis

CCAC Theses and Dissertations

With much effort being placed on the physical, procedural, and technological solutions for Information Systems (IS) cybersecurity, research studies tend to focus their efforts on large organizations while overlooking very smaller organizations (below 50 employees). This study addressed the failure to prevent data breaches in Very Small Enterprises (VSEs). VSEs contribute significantly to the economy, however, are more prone to cyber-attacks due to the limited risk mitigations on their systems and low cybersecurity skills of their employees. VSEs utilize Point-of-Sale (POS) systems that are exposed to cyberspace, however, they are often not equipped to prevent complex cybersecurity issues that can …

Implementing Algorithmic Crisis Alerts In Mhealth Systems For Veterans With Ptsd, Md Sazzad Hossain, Priyanka Annapureddy, Sheikh Iqbal Ahamed, Praveen Madiraju, Mark Flower, Lisa Rein, Thomas Kissane, Wylie Frydrychowicz, Naveen K. Bansal, Niharika Jain, Katinka Hooyer, Zeno Franco

Computer Science Faculty Research and Publications

This paper seeks to establish a machine learning driven method by which a military veteran with Post-Traumatic Stress Disorder (PTSD) is classified as being in a crisis situation or not, based upon a given set of criteria. Optimizing alerting decision rules is critical to ensure that veterans at highest risk for mental health crisis rapidly receive additional attention. Subject matter experts in our team (a psychologist, a medical anthropologist, and an expert veteran), defined acute crisis, early warning signs and long-term crisis from this dataset. First, we used a decision tree to find an early time point when the peer …

A Quantum Algorithm For Automata Encoding, Edison Tsai, Marek Perkowski

Electrical and Computer Engineering Faculty Publications and Presentations

Encoding of finite automata or state machines is critical to modern digital logic design methods for sequential circuits. Encoding is the process of assigning to every state, input value, and output value of a state machine a binary string, which is used to represent that state, input value, or output value in digital logic. Usually, one wishes to choose an encoding that, when the state machine is implemented as a digital logic circuit, will optimize some aspect of that circuit. For instance, one might wish to encode in such a way as to minimize power dissipation or silicon area. For …

Disaster Damage Categorization Applying Satellite Images And Machine Learning Algorithm, Farinaz Sabz Ali Pour, Adrian Gheorghe

Engineering Management & Systems Engineering Faculty Publications

Special information has a significant role in disaster management. Land cover mapping can detect short- and long-term changes and monitor the vulnerable habitats. It is an effective evaluation to be included in the disaster management system to protect the conservation areas. The critical visual and statistical information presented to the decision-makers can help in mitigation or adaption before crossing a threshold. This paper aims to contribute in the academic and the practice aspects by offering a potential solution to enhance the disaster data source effectiveness. The key research question that the authors try to answer in this paper is how …

Direct And Constructivist Instructional Design: A Comparison Of Efficiency Using Mental Workload And Task Performance, Giuliana Orru, Luca Longo

Conference papers

This paper investigates the efficiency of two instructional design conditions: a traditional design based on the direct instruction approach to learning and its extension with a collaborative activity based upon the community of inquiry approach to learning. This activity was built upon a set of textual trigger questions to elicit cognitive abilities and support knowledge formation. A total of 115 students participated in the experiments and a number of third-level computer science classes where divided in two groups. A control group of learners received the former instructional design while an experimental group also received the latter design. Subsequently, learners of …

Contextual Healing: Privacy Through Interpretation Management, Fatma Outay, Rula Sayaf

All Works

Contextual privacy is an essential concept in social software communication. Managing privacy of data disclosed in social software dependence strongly on the context the data is disclosed in. The sheer amount of posts and audiences may lead to context ambiguity. Ambiguity can affect contextual privacy management and effective communication. Current contextual privacy management approaches can be either too complex to use, or too simple to offer fine-grained control. In many cases, it is challenging to strike a balance between effective control and ease-of-use. In this article, we analyse contextual privacy by in relation to context and communication. We examine a …

A Review Of Challenges And Barriers Implementing Rfid Technology In The Healthcare Sector, Ahed Abugabah, Nishara Nizamuddin, Alaa Abuqabbeh

All Works

© 2020 The Authors. Published by Elsevier B.V.All rights reserved. The healthcare industry is progressively involved in adopting new technologies to provide improved quality of care given to patients. The implementation of RFID technology has globally impacted several industries and this revolution has improved the aspects of service delivery in the healthcare industry as well. The RFID technology has the potential to track medical assets and interact with almost any of the medical devices, pharmaceutical materials, IT equipment, or individual patients, deployed in hospitals all over the world. The motivation behind this paper is to investigate the advantages and obstacles …

Landing Throttleable Hybrid Rockets With Hierarchical Reinforcement Learning In A Simulated Environment, Francesco Alessandro Stefano Mikulis-Borsoi

Honors Theses and Capstones

In this paper, I develop a hierarchical Markov Decision Process (MDP) structure for completing the task of vertical rocket landing. I start by covering the background of this problem, and formally defining its constraints. In order to reduce mistakes while formulating different MDPs, I define and develop the criteria for a standardized MDP definition format. I then decompose the problem into several sub-problems of vertical landing, namely velocity control and vertical stability control. By exploiting MDP coupling and symmetrical properties, I am able to significantly reduce the size of the state space compared to a unified MDP formulation. This paper …

A Probabilistic Machine Learning Framework For Cloud Resource Selection On The Cloud, Syeduzzaman Khan

University of the Pacific Theses and Dissertations

The execution of the scientific applications on the Cloud comes with great flexibility, scalability, cost-effectiveness, and substantial computing power. Market-leading Cloud service providers such as Amazon Web service (AWS), Azure, Google Cloud Platform (GCP) offer various general purposes, memory-intensive, and compute-intensive Cloud instances for the execution of scientific applications. The scientific community, especially small research institutions and undergraduate universities, face many hurdles while conducting high-performance computing research in the absence of large dedicated clusters. The Cloud provides a lucrative alternative to dedicated clusters, however a wide range of Cloud computing choices makes the instance selection for the end-users. This thesis …

Design And Development Of Electronic Sensor And Monitoring System Of Smart Low-Cost Phototherapy Light System For Non-Invasive Monitoring And Treatment Of Neonatal Jaundice, Paul M. Cabacungan, Carlos Oppus, Gregory L. Tangonan, Nerissa G. Cabacungan, John Paul Mamaradlo, Neil Angelo Mercado

Electronics, Computer, and Communications Engineering Faculty Publications

This paper showcases our previous and continuously improving development at Ateneo Innovation Center (AIC) and partners in designing and further enhancing the existing Low-cost Phototherapy Light System (LPLS) and Improved Low-cost Phototherapy Light System (ILPLS) to the new Smart Low-cost Phototherapy Light System (Smart LPLS) with non-invasive jaundice monitoring for newborns with Neonatal Jaundice (NNJ). Developing this tool will help determine the intensity of yellowish color in infants and can monitor NNJ in a non-invasive way. The system is envisioned to be integrated with Mobile or Near Cloud as part of Smart Nursing Station together with other hospital equipment for …

Towards A Context-Aware Knowledge Model For Smart Service Systems, Thang Le Dinh, Thanh Thoa Pham Thi, Cuong Pham-Nguyen, Hoai Nam Le Nguyen

Conference papers

The advancement of the Internet of things, big data, and mobile computing leads to the need for smart services that enable the context awareness and the adaptability to their changing contexts. Today, designing a smart service system is a complex task due to the lack of an adequate model support in awareness and pervasive environment. In this paper, we present a context-aware knowledge model for smart service systems that organizes the domain and context-aware knowledge into knowledge components based on the three levels of services: Services, Service system and Network of service systems. The context-aware knowledge model for smart service …

Competencies For Educators In Delivering Digital Accessibility In Higher Education, John Gilligan

Conference Papers

The aim of this paper is to critically review the capabilities of the European Framework for the Digital Competence of Educators (DigCompEdu) and the UNESCO ICT Competency Framework for in delivering greater accessibility for students with disabilities in a Higher Education landscape undergoing Digital Transformation. These frameworks describe what it means for educators to be digitally competent. However are there other competencies required to deliver Digital Accessibility in education. The particular focus of this paper is the role of the teachers in delivering Digital Accessibility in higher education. What should be expected of them and what are the required competencies …

Named Entity Recognition Based On A Graph Structure, David Muñoz, Fernando Pérez Téllez, David Pinto

Articles

The identification of indirect relationships between texts from different sources makes the task of text mining useful when the goal is to obtain the most valuable information from a set of texts. That is why in the field of information retrieval the correct recognition of named entities plays an important role when extracting valuable information in large amounts of text. Therefore, it is important to propose techniques that improve the NER classifiers in order to achieve the correct recognition of named entities. In this work, a graph structure for storage and enrichment of named entities is proposed. It makes use …

Exploring Composite Dataset Biases For Heart Sound Classification, Davoud Shariat Panah, Andrew Hines, Susan Mckeever

Conference papers

In the last few years, the automatic classification of heart sounds has been widely studied as a screening method for heart disease. Some of these studies have achieved high accuracies in heart abnormality prediction. However, for such models to assist clinicians in the detection of heart abnormalities, it is of critical importance that they are generalisable, working on unseen real-world data. Despite the importance of generalisability, the presence of bias in the leading heart sound datasets used in these studies has remained unexplored. In this paper, we explore the presence of potential bias in heart sound datasets. Using a small …

Synthesising Tabular Datasets Using Wasserstein Conditional Gans With Gradient Penalty (Wcgan-Gp), Manhar Singh Walia, Brendan Tierney, Susan Mckeever

Conference papers

Deep learning based methods based on Generative Adversarial Networks (GANs) have seen remarkable success in data synthesis of images and text. This study investigates the use of GANs for the generation of tabular mixed dataset. We apply Wasserstein Conditional Generative Adversarial Network (WCGAN-GP) to the task of generating tabular synthetic data that is indistinguishable from the real data, without incurring information leakage. The performance of WCGAN-GP is compared against both the ground truth datasets and SMOTE using three labelled real-world datasets from different domains. Our results for WCGAN-GP show that the synthetic data preserves distributions and relationships of the real …

A Framework For Validating And Testing Agent-Based Models: A Case Study From Infectious Diseases Modelling., Elizabeth Hunter, John D. Kelleher

Conference papers

In this paper we present a framework for validating and testing an agent-based model that includes determining the appropriate number of runs to account for variation in model runs, validating the model and showing that the model can be used to learn about the system. To demonstrate the framework we use a case study of an agent-based model for the spread of infectious diseases.

Mutual Information Decay Curves And Hyper-Parameter Grid Search Design For Recurrent Neural Architectures, Abhijit Mahalunkar, John Kelleher

Conference papers

We present an approach to design the grid searches for hyper-parameter optimization for recurrent neural architectures. The basis for this approach is the use of mutual information to analyze long distance dependencies (LDDs) within a dataset. We also report a set of experiments that demonstrate how using this approach, we obtain state-of-the-art results for DilatedRNNs across a range of benchmark datasets.