Computer Sciences Commons^™

Learning From Experience: An Automatic Ph Neutralization System Using Hybrid Fuzzy System And Neural Network, Ethar H.K. Alkamil, Seaar Al-Dabooni, Ahmed K. Abbas, Ralph Flori, Donald C. Wunsch

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

In oil and gas industry, the pH level is one of the most important indicators of mud contamination while drilling. Although the process has simple components, the pH neutralization process is complicated in the mud circulation system. This difficulty is due to the high nonlinearity of the process. In this paper, the fuzzy neural network (FNN) is integrated to the fuzzy logic controller (FLC) to create a system which identifies pH fluctuation into a drilling mud system, assesses and signals its severity. And then, it automatically actuates a chemical treating fluids valve (CTFV), such as Caustic Soda (CS), for neutralizing …

Can Ego Defense Mechanism Help Explain Is Security Dysfunctional Behavior, Abhijit Chaudhury, Debasish Mallick

Information Systems and Analytics Department Faculty Conference Proceedings

IS security behavior studies are becoming popular. To date, much of the research has been based on theories such as the Theory of Planned Behavior, Technology Adoption Model, Rational Choice theory and Theory of Reasoned Action. They view users as rational individuals making conscious utilitarian decisions when there is increasing evidence that security breaches are the result of human behavior such as carelessness, malicious intent, bad habits, and hostility. We propose the ego defense mechanism model, taken from the psychoanalytical world. This model makes no assumption of rationality and has been developed to help understand the roots of dysfunctional behavior …

International And Interdisciplinary Perspectives On Children & Recommender Systems (Kidrec), Jerry Alan Fails, Maria Soledad Pera, Natalia Kucirkova, Franca Garzotto

Computer Science Faculty Publications and Presentations

Resources for children are abundant, but finding suitable and appropriate resources for children in our information-rich society can be challenging. Due to this abundance of information, systems to find and recommend appropriate information for children are needed. Recommender systems (RS) for children have only recently begun to be researched. This area of research brings together researchers in education, child-development, computer scientists, designers, and more who address several issues including those related to education, algorithms, ethics, privacy, security. In this workshop we will: discuss and identify issues related to RS designed for children including challenges and limitations, discuss possible solutions to …

Mechanism Design, Matching Theory And The Stable Roommates Problem, Yashaswi Mohanty

Honors Theses

This thesis consists of two independent albeit related chapters. The first chapter introduces concepts from mechanism design and matching theory, and discusses potential applications of this theory, particularly in relation to dorm allocations in colleges. The second chapter investigates a subset of the dorm allocation problem, namely that of matching roommates. In particular, the paper looks at the probability of solvability of random instances of the stable roommates game under the condition that preferences are not completely random and exogenous but endogenously determined through a dependence on room choice. These probabilities are estimated using Monte-Carlo simulations and then compared with …

Developing A Cyberterrorism Policy: Incorporating Individual Values, Osama Bassam J. Rabie

Theses and Dissertations

Preventing cyberterrorism is becoming a necessity for individuals, organizations, and governments. However, current policies focus on technical and managerial aspects without asking for experts and non-experts values and preferences for preventing cyberterrorism. This study employs value focused thinking and public value forum to bare strategic measures and alternatives for complex policy decisions for preventing cyberterrorism. The strategic measures and alternatives are per socio-technical process.

Optimization For Structural Equation Modeling: Applications To Substance Use Disorders, Mahsa Zahery

Theses and Dissertations

Substance abuse is a serious issue in both modern and traditional societies. Besides health complications such as depression, cancer and HIV, social complications such as loss of concentration, loss of job, and legal problems are among the numerous hazards substance use disorder imposes on societies. Understanding the causes of substance abuse and preventing its negative effects continues to be the focus of much research.

Substance use behaviors, symptoms and signs are usually measured in form of ordinal data, which are often modeled under threshold models in Structural Equation Modeling (SEM). In this dissertation, we have developed a general nonlinear optimizer …

Security Vulnerabilities In Android Applications, Crischell Montealegre, Charles Rubia Njuguna, Muhammad Imran Malik, Peter Hannay, Ian Noel Mcateer

Australian Information Security Management Conference

Privacy-related vulnerabilities and risks are often embedded into applications during their development, with this action being either performed out of malice or out of negligence. Moreover, the majority of the mobile applications initiate connections to websites, other apps, or services outside of its scope causing significant compromise to the oblivious user. Therefore, mobile data encryption or related data-protection controls should be taken into account during the application development phase. This paper evaluates some standard apps and their associated threats using publicly available tools and demonstrates how an ignorant user or an organisation can fall prey to such apps.

Xmpp Architecture And Security Challenges In An Iot Ecosystem, Muhammad Imran Malik, Ian Noel Mcateer, Peter Hannay, Syed Naeem Firdous, Zubair Baig

Australian Information Security Management Conference

The elusive quest for technological advancements with the aim to make human life easier has led to the development of the Internet of Things (IoT). IoT technology holds the potential to revolutionise our daily life, but not before overcoming barriers of security and data protection. IoTs’ steered a new era of free information that transformed life in ways that one could not imagine a decade ago. Hence, humans have started considering IoTs as a pervasive technology. This digital transformation does not stop here as the new wave of IoT is not about people, rather it is about intelligent connected devices. …

Caregiver Assessment Using Smart Gaming Technology: A Preliminary Approach, Garrett Goodman, Tanvi Banerjee, William Romine, Cogan Shimizu, Jennifer Hughes

Computer Science and Engineering Faculty Publications

As pre-diagnostic technologies are becoming increasingly accessible, using them to improve the quality of care available to dementia patients and their caregivers is of increasing interest. Specifically, we aim to develop a tool for non- invasively assessing task performance in a simple gaming application. To address this, we have developed Caregiver Assessment using Smart Technology (CAST), a mobile application that personalizes a traditional word scramble game. Its core functionality uses a Fuzzy Inference System (FIS) optimized via a Genetic Algorithm (GA) to provide customized performance measures for each user of the system. With CAST, we match the relative level of …

Multilabel Learning For The Online Transient Stability Assessment Of Electric Power Systems, Peyman Beyranvand, Veysel Murat İstemi̇han Genç, Zehra Çataltepe

Turkish Journal of Electrical Engineering and Computer Sciences

Dynamic security assessment of a large power system operating over a wide range of conditions requires an intensive computation for evaluating the system's transient stability against a large number of contingencies. In this study, we investigate the application of multilabel learning for improving training and prediction time, along with the prediction accuracy, of neural networks for online transient stability assessment of power systems. We introduce a new multilabel learning method, which uses a contingency clustering step to learn similar contingencies together in the same multilabel multilayer perceptron. Experimental results on two different power systems demonstrate improved accuracy, as well as …

Novel Modified Impedance-Based Methods For Fault Location In The Presence Of A Fault Current Limiter, Javad Barati, Aref Doroudi

Turkish Journal of Electrical Engineering and Computer Sciences

A fault current limiter (FCL) is promising novel electric equipment to effectively reduce excessive short circuit current in power networks. The presence of a FCL at the time of a fault occurrence makes it necessary to consider new settings for protective relays and fault locators. This paper examines the presence of a FCL in power networks and its effects on single-ended impedance-based fault location methods. It will be shown that FCL deployment in a transmission line makes the traditional fault location method inefficient. Two modified methods are presented to solve the problem. The modified methods locate the fault point using …

A Grounded Theory Of Emergent Leadership In Nonhierarchical Virtual Teams, Randall Fleming

Walden Dissertations and Doctoral Studies

A Grounded Theory of Emergent Leadership in Nonhierarchical Virtual Teams

by

Randall David Fleming

MS, Colorado Technical University, 2008

BA, The Ohio State University, 1984

Dissertation Submitted in Partial Fulfillment

of the Requirements for the Degree of

Doctor of Philosophy

Management

Walden University

November 2018

Exploring Welfare Recipients' Self-Sufficiency Barriers Through Information Management Systems In Tennessee, Valenta Eunice Nichols

Walden Dissertations and Doctoral Studies

Families living on welfare in low-income impoverished neighborhoods encounter multiple barriers that need mitigating before seeking work to reach self-sufficiency. Many welfare recipients' self-sufficiency barriers are unnoticeable to caseworkers due to lack of data sharing to assess clients' needs through information technology processes. The purpose of this exploratory descriptive phenomenological qualitative study was to understand welfare recipients' viewpoints on socioeconomic barriers to living self-sufficiently and gain perspectives from human services caseworkers and technical resources on data sharing issues that impact recipients' ability to live independently from government assistance. Data collection and observational field notes resulted from in-depth interviews of 11 …

The Relationship Between Leadership Style And Cognitive Style To Software Project Success, Jacquelyne L. Wilson

Walden Dissertations and Doctoral Studies

Project managers can be change agents providing direction and motivation for subordinates to meet and exceed goals; however, there is a lack of information about the soft skills needed to achieve project success. Understanding the relationship between cognitive style and transformational leadership to software project outcomes is important. This study describes the lived experiences of software project managers by focusing on their attitudes towards, perceptions of, and behaviors related to using transformational leadership and cognitive styles in agile software development environments. Husserlian phenomenological design was used to identify the structure of participants' experiences. The naturalistic decision-making model and the theory …

Overcoming Data Breaches And Human Factors In Minimizing Threats To Cyber-Security Ecosystems, Manouan Pierre-Marius Ayereby

Walden Dissertations and Doctoral Studies

This mixed-methods study focused on the internal human factors responsible for data breaches that could cause adverse impacts on organizations. Based on the Swiss cheese theory, the study was designed to examine preventative measures that managers could implement to minimize potential data breaches resulting from internal employees' behaviors. The purpose of this study was to provide insight to managers about developing strategies that could prevent data breaches from cyber-threats by focusing on the specific internal human factors responsible for data breaches, the root causes, and the preventive measures that could minimize threats from internal employees. Data were collected from 10 …

Relationship Between Software Development Team Structure, Ambiguity, Volatility, And Project Failure, Dominic Martinelli Saxton

Walden Dissertations and Doctoral Studies

Complex environments like the United States Air Force's advanced weapon systems are highly reliant on externally developed software, which is often delivered late, over budget, and with fewer benefits than expected. Grounded in Galbraith's organizational information processing theory, the purpose of this correlational study was to examine the relationship between software development team structure, ambiguity, volatility and software project failure. Participants included 23 members of the Armed Forces Communications and Electronics Association in the southeastern United States who completed 4 project management surveys. Results of multiple regression analysis indicated the model as a whole was able to predict software project …

Digital Strategies Senior Bank Executives In Mauritius Use To Improve Customer Service, Sailesh Sewpaul

Walden Dissertations and Doctoral Studies

Customers' use of digital banking has reshaped traditional banking, and senior level bank executives must know how to leverage this innovation to improve customer service to increase profitability. Using the technology acceptance model as the conceptual framework, the purpose of this multiple case study was to explore effective digital banking strategies that senior level executives used to improve customer service to increase profitability. The target population for this study included senior-level executives from 3 banks in Mauritius possessing successful development and implementation experience in digital banking strategies to improve customer service. Data were collected through semistructured interviews and organizational documents, …

Linguistic Characteristics Of Censorable Language On Sinaweibo, Kei Yin Ng, Anna Feldman, Jing Peng, Christopher Leberknight

Department of Computer Science Faculty Scholarship and Creative Works

This paper investigates censorship from a linguistic perspective. We collect a corpus of censored and uncensored posts on a number of topics, build a classifier that predicts censorship decisions independent of discussion topics. Our investigation reveals that the strongest linguistic indicator of censored content of our corpus is its readability.

A Performance Comparison Of Neural Network And Svm Classifiers Using Eeg Spectral Features To Predict Epileptic Seizures, Ian Thomas Tennant Watson

Dissertations

Epilepsy is one of the most common neurological disorders, and afflicts approximately 70 million people globally. 30-40% of patients have refractory epilepsy, where seizures cannot be controlled by anti-epileptic medication, and surgery is neither appropriate, nor available. The unpredictable nature of epileptic seizures is the primary cause of mortality among patients, and leads to significant psychosocial disability. If seizures could be predicted in advance, automatic seizure warning systems could transform the lives of millions of people. This study presents a performance comparison of artificial neural network and sup port vector machine classifiers, using EEG spectral features to predict the onset …

Classification Using Association Rules, Colin Kane

Dissertations

This research investigates the use of an unsupervised learning technique, association rules, to make class predictions. The use of association rules to make class predictions is a growing area of focus within data mining research. The research to date has focused predominately on balanced datasets or synthetized imbalanced datasets. There have been concerns raised that the algorithms using association rules to make classifications do not perform well on imbalanced datasets. This research comprehensively evaluates the accuracy of a number of association rule classifiers in predicting home loan sales in an Irish retail banking context. The experiments designed test three associative …

Automatic Table Extension With Open Data, Benedikt Kleppmann

Dissertations

With thousands of data sources available on the web as well as within organisations, data scientists increasingly spend more time searching for data than analysing it. To ease the task of find and integrating relevant data for data mining projects, this dissertation presents two new methods for automatic table extension. Automatic table extension systems take over the task of tata discovery and data integration by adding new columns with new information (new attributes) to any table. The data values in the new columns are extracted from a given corpus of tables.

Wi-Fi Finger-Printing Based Indoor Localization Using Nano-Scale Unmanned Aerial Vehicles, Appala Narasimha Raju Chekuri

Electronic Theses and Dissertations

Explosive growth in the number of mobile devices like smartphones, tablets, and smartwatches has escalated the demand for localization-based services, spurring development of numerous indoor localization techniques. Especially, widespread deployment of wireless LANs prompted ever increasing interests in WiFi-based indoor localization mechanisms. However, a critical shortcoming of such localization schemes is the intensive time and labor requirements for collecting and building the WiFi fingerprinting database, especially when the system needs to cover a large space. In this thesis, we propose to automate the WiFi fingerprint survey process using a group of nano-scale unmanned aerial vehicles (NAVs). The proposed system significantly …

Decreasing Occlusion And Increasing Explanation In Interactive Visual Knowledge Discovery, Abdulrahman Ahmed Gharawi

All Master's Theses

Lack of explanation and occlusion are the major problems for interactive visual knowledge discovery, machine learning and data mining in multidimensional data. This thesis proposes a hybrid method that combines visual and analytical means to deal with these problems. This method, denoted as FSP, uses visualization of n-D data in 2-D in a set of Shifted Paired Coordinates (SPC). SPC for n-D data consists of n/2 pairs of Cartesian coordinates that are shifted relative to each other to avoid their overlap. Each n-D point is represented as a directed graph in SPC. It is shown that the FSP method simplifies …

Scalable And Secure Provenance Querying For Scientific Workflows And Its Application In Autism Study, Fahima Amin Bhuyan

Wayne State University Dissertations

In the era of big data, scientific workflows have become essential to automate scientific experiments and guarantee repeatability. As both data and workflow increase in their scale, requirements for having a data lineage management system commensurate with the complexity of the workflow also become necessary, calling for new scalable storage, query, and analytics infrastructure. This system that manages and preserves the derivation history and morphosis of data, known as provenance system, is essential for maintaining quality and trustworthiness of data products and ensuring reproducibility of scientific discoveries. With a flurry of research and increased adoption of scientific workflows in processing …

Programming: Predicting Student Success Early In Cs1. A Re-Validation And Replication Study, Keith Quille, Susan Bergin

Articles

This paper describes a large, multi-institutional revalidation study conducted in the academic year 2015-16. Six hundred and ninetytwo students participated in this study, from 11 institutions (ten institutions in Ireland and one in Denmark). The primary goal was to validate and further develop an existing computational prediction model called Predict Student Success (PreSS). In doing so, this study addressed a call from the 2015 ITiCSE working group (the second "Grand Challenge"), to "systematically analyse and verify previous studies using data from multiple contexts to tease out tacit factors that contribute to previously observed outcomes". PreSS was developed and validated in …

Hypothesis Only Baselines In Natural Language Inference, Adam Poliak, Jason Naradowsky, Aparajita Haldar, Rachel Rudinger, Benjamin Van Durme

Computer Science Faculty Research and Scholarship

We propose a hypothesis only baseline for diagnosing Natural Language Inference (NLI). Especially when an NLI dataset assumes inference is occurring based purely on the relationship between a context and a hypothesis, it follows that assessing entailment relations while ignoring the provided context is a degenerate solution. Yet, through experiments on 10 distinct NLI datasets, we find that this approach, which we refer to as a hypothesis-only model, is able to significantly outperform a majority-class baseline across a number of NLI datasets. Our analysis suggests that statistical irregularities may allow a model to perform NLI in some datasets beyond what …

Collecting Diverse Natural Language Inference Problems For Sentence Representation Evaluation, Adam Poliak, Aparajita Haldar, Rachel Rudinger, J Edward Hu, Ellie Pavlick, Aaron Steven White, Benjamin Van Durme

Computer Science Faculty Research and Scholarship

We present a large-scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation captures distinct types of reasoning. The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in over half a million labeled context-hypothesis pairs in total. We refer to our collection as the DNC: Diverse Natural Language Inference Collection. The DNC is available online at https://www.decomp.net, and will grow over time as additional resources are recast and added from novel sources.

Design Of Cloud Based Robots Using Big Data Analytics And Neuromorphic Computing, Ashwin Satyanarayana, Janusz Kusyk, Yu-Wen Chen

Publications and Research

Understanding the brain is perhaps one of the greatest challenges facing twenty-first century science. While a traditional computer excels in precision and unbiased logic, its abilities to interact socially lags behind those of biological neural systems. Recent technologies, such as neuromorphic engineering, cloud infrastructure, and big data analytics, have emerged that can narrow the gap between traditional robots and human intelligence. Neuromorphic robotics mimicking brain functions can contribute in developing intelligent machines capable of learning and making autonomous decisions. Cloud-based robotics take advantage of remote resources for parallel computation and sharing large amounts of information while benefiting from analysis of …

Parallelization And Scalability Analysis Of The \\[1pc] 3d Spatially Variant Lattice Algorithm, Henry Roger Moncada Lopez

Open Access Theses & Dissertations

The purpose of this research is to design a faster implementation of an algorithm to generate 3D spatially variant lattices (SVL) and improve its performance when it is running on a parallel computer system. The algorithm is used to synthesize a SVL for a periodic structure. The algorithm has the ability to spatially vary the unit cell, the orientation of the unit cells, lattice spacing, fill fraction, material composition, and lattice symmetry. The algorithm produces a lattice that is smooth, continuous and free of defects. The lattice spacing remains strikingly uniform even when the lattice is spatially varied. This is …

Generating Diverse And Meaningful Captions: Unsupervised Specificity Optimization For Image Captioning, Annika Lindh, Robert J. Ross, Abhijit Mahalunkar, Giancarlo Salton, John D. Kelleher

Conference papers

Image Captioning is a task that requires models to acquire a multi-modal understanding of the world and to express this understanding in natural language text. While the state-of-the-art for this task has rapidly improved in terms of n-gram metrics, these models tend to output the same generic captions for similar images. In this work, we address this limitation and train a model that generates more diverse and specific captions through an unsupervised training approach that incorporates a learning signal from an Image Retrieval model. We summarize previous results and improve the state-of-the-art on caption diversity and novelty.

We make our …