Computer Sciences Commons^™

Artificial Neural Network Models For Pattern Discovery From Ecg Time Series, Mehakpreet Kaur

College of Graduate Studies: Theses & Dissertations

Artificial Neural Network (ANN) models have recently become de facto models for deep learning with a wide range of applications spanning from scientific fields such as computer vision, physics, biology, medicine to social life (suggesting preferred movies, shopping lists, etc.). Due to advancements in computer technology and the increased practice of Artificial Intelligence (AI) in medicine and biological research, ANNs have been extensively applied not only to provide quick information about diseases, but also to make diagnostics accurate and cost-effective. We propose an ANN-based model to analyze a patient's electrocardiogram (ECG) data and produce accurate diagnostics regarding possible heart diseases …

No Soldiers Left Behind: An Iot-Based Low-Power Military Mobile Health System Design, James Jin Kang, Wencheng Yang, Gordana Dermody, Mohammadreza Ghasemian, Sasan Adibi, Paul Haskell-Dowland

Research outputs 2014 to 2021

© 2013 IEEE. There has been an increasing prevalence of ad-hoc networks for various purposes and applications. These include Low Power Wide Area Networks (LPWAN) and Wireless Body Area Networks (WBAN) which have emerging applications in health monitoring as well as user location tracking in emergency settings. Further applications can include real-Time actuation of IoT equipment, and activation of emergency alarms through the inference of a user's situation using sensors and personal devices through a LPWAN. This has potential benefits for military networks and applications regarding the health of soldiers and field personnel during a mission. Due to the wireless …

Quantifiable Isovist And Graph-Based Measures For Automatic Evaluation Of Different Area Types In Virtual Terrain Generation, Andrew Pech, Chiou Peng Lam, Martin Masek

Research outputs 2014 to 2021

© 2013 IEEE. This article describes a set of proposed measures for characterizing areas within a virtual terrain in terms of their attributes and their relationships with other areas for incorporating game designers' intent in gameplay requirement-based terrain generation. Examples of such gameplay elements include vantage point, strongholds, chokepoints and hidden areas. Our measures are constructed on characteristics of an isovist, that is, the volume of visible space at a local area and the connectivity of areas within the terrain. The calculation of these measures is detailed, in particular we introduce two new ways to accurately and efficiently calculate the …

Quantifying The Need For Supervised Machine Learning In Conducting Live Forensic Analysis Of Emergent Configurations (Eco) In Iot Environments, Victor R. Kebande, Richard A. Ikuesan, Nickson M. Karie, Sadi Alawadi, Kim-Kwang Raymond Choo, Arafat Al-Dhaqm

Research outputs 2014 to 2021

© 2020 The Author(s) Machine learning has been shown as a promising approach to mine larger datasets, such as those that comprise data from a broad range of Internet of Things devices, across complex environment(s) to solve different problems. This paper surveys existing literature on the potential of using supervised classical machine learning techniques, such as K-Nearest Neigbour, Support Vector Machines, Naive Bayes and Random Forest algorithms, in performing live digital forensics for different IoT configurations. There are also a number of challenges associated with the use of machine learning techniques, as discussed in this paper.

Sam-Sos: A Stochastic Software Architecture Modeling And Verification Approach For Complex System-Of-Systems, Ahmad Mohsin, Naeem Khalid Janjua, Syed M. S. Islam, Muhammad Ali Babar

Research outputs 2014 to 2021

A System-of-Systems (SoS) is a complex, dynamic system whose Constituent Systems (CSs) are not known precisely at design time, and the environment in which they operate is uncertain. SoS behavior is unpredictable due to underlying architectural characteristics such as autonomy and independence. Although the stochastic composition of CSs is vital to achieving SoS missions, their unknown behaviors and impact on system properties are unavoidable. Moreover, unknown conditions and volatility have significant effects on crucial Quality Attributes (QAs) such as performance, reliability and security. Hence, the structure and behavior of a SoS must be modeled and validated quantitatively to foresee any …

A Comprehensive Analysis Of Smart Ship Systems And Underlying Cybersecurity Issues, Dennis Bothur

Theses : Honours

The maritime domain benefits greatly from advanced technology and ubiquitous connectivity. From “smart” sensors to “augmented reality”, the opportunities to save costs and improve safety are endless. The aim of this dissertation is to study the capabilities of smart ship systems in the context of Internet-of-Things and analyse the potential cybersecurity risks and challenges that smart technologies may introduce into this accelerating digital economy.

The first part of this work investigates the architecture of a “Smart Ship System” and the primary subsystems, including the integrated bridge, navigation and communication systems, networking, operational systems, and sensor networks. The mapping of the …

Pretraining Deep Learning Models For Natural Language Understanding, Han Shao

Honors Papers

Since the first bidirectional deep learn- ing model for natural language understanding, BERT, emerged in 2018, researchers have started to study and use pretrained bidirectional autoencoding or autoregressive models to solve language problems. In this project, I conducted research to fully understand BERT and XLNet and applied their pretrained models to two language tasks: reading comprehension (RACE) and part-of-speech tagging (The Penn Treebank). After experimenting with those released models, I implemented my own version of ELECTRA, a pretrained text encoder as a discriminator instead of a generator to improve compute-efficiency, with BERT as its underlying architecture. To reduce the number …

Asking Questions Is Easy, Asking Great Questions Is Hard: Constructing Effective Stack Overflow Questions, Jane W. Hsieh

Honors Papers

This paper explores and seeks to improve the ways in which Stack Overflow question posts can elicit answers. Using statistical data analysis approaches and reviews of existing literature, we pin- point three key factors that are found in many previously success- ful/answerable questions. We then present a prototypical sidebar for the ask page that leverages these factors to dynamically (1) evaluate the quality of questions in construction (2) display answer previews of relevant questions and (3) scaffold the identified factors to subsequent askers during their question development processes.

Systematic Classification Of Attackers Via Bounded Model Checking, Eric Rothstein-Morris, Jun Sun, Sudipta Chattopadyay

Research Collection School Of Computing and Information Systems

In this work, we study the problem of verification of systems in the presence of attackers using bounded model checking. Given a system and a set of security requirements, we present a methodology to generate and classify attackers, mapping them to the set of requirements that they can break. A naive approach suffers from the same shortcomings of any large model checking problem, i.e., memory shortage and exponential time. To cope with these shortcomings, we describe two sound heuristics based on cone-of-influence reduction and on learning, which we demonstrate empirically by applying our methodology to a set of hardware benchmark …

Practical Server-Side Indoor Localization: Tackling Cardinality Outlier Challenges, Anuradha Ravi, Archan Misra

Research Collection School Of Computing and Information Systems

In spite of many advances in indoor localization techniques, practical implementation of robust device independent, server-side Wi-Fi localization (i.e., without any active participation of client devices) remains a challenge. This work utilizes an operationally-deployed Wi-Fi based indoor location infrastructure, based on the classical RADAR algorithm, to tackle two such practical challenges: (a) low cardinality, whereby only the associated AP generates sufficient RSSI reports and (b) outlier identification, which requires explicit identification of mobile clients that are attached to the Wi-Fi network but outside the fingerprinted region. To tackle the low-cardinality problem, we present a technique that uses cardinality changes to …

Scalable, Adaptable And Fast Estimation Of Transient Downtime In Virtual Infrastructures Using Convex Decomposition And Sample Path Randomization, Zhiling Guo, Jin Li, Ram Ramesh

Research Collection School Of Computing and Information Systems

Network function virtualization enables efficient cloud-resource planning by virtualizing network services and applications into software running on commodity servers. A cloud-service provider needs to manage and ensure service availability of a network of concurrent virtualized network functions (VNFs). The downtime distribution of a network of VNFs can be estimated using sample-path randomization on the underlying birth–death process. An integrated modeling approach for this purpose is limited by its scalability and computational load because of the high dimensionality of the integrated birth–death process. We propose a generalized convex decomposition of the integrated birth-death process, which transforms the high-dimensional multi-VNF process into …

Server-Aided Revocable Attribute-Based Encryption For Cloud Computing Services, Hui Cui, Tsz Hon Yuen, Robert H. Deng, Guilin Wang

Research Collection School Of Computing and Information Systems

Attribute-based encryption (ABE) has been regarded as a promising solution in cloud computing services to enable scalable access control without compromising the security. Despite of the advantages, efficient user revocation has been a challenge in ABE. One suggestion for user revocation is using the binary tree in the key generation phase of an ABE scheme, which enables a trusted key generation center to periodically distribute the key update information to all nonrevoked users over a public channel. This revocation approach reduces the size of key updates from linear to logarithmic in the number of users. But it requires each user …

Spatial Multi-Objective Land Use Optimization Toward Livability Based On Boundary-Based Genetic Algorithm: A Case Study In Singapore, Kai Cao, Muyang Liu, Shu Wang, Mengqi Liu, Wenting Zhang, Qiang Meng, Bo Huang

Research Collection School Of Computing and Information Systems

In this research, the concept of livability has been quantitatively and comprehensively reviewed and interpreted to contribute to spatial multi-objective land use optimization modelling. In addition, a multi-objective land use optimization model was constructed using goal programming and a weighted-sum approach, followed by a boundary-based genetic algorithm adapted to help address the spatial multi-objective land use optimization problem. Furthermore, the model is successfully and effectively applied to the case study in the Central Region of Queenstown Planning Area of Singapore towards livability. In the case study, the experiments based on equal weights and experiments based on different weights combination have …

Structure-Priority Image Restoration Through Genetic Algorithm Optimization, Zhaoxia Wang, Haibo Pen, Ting Yang, Quan Wang

Research Collection School Of Computing and Information Systems

With the significant increase in the use of image information, image restoration has been gaining much attention by researchers. Restoring the structural information as well as the textural information of a damaged image to produce visually plausible restorations is a challenging task. Genetic algorithm (GA) and its variants have been applied in many fields due to their global optimization capabilities. However, the applications of GA to the image restoration domain still remain an emerging discipline. It is still challenging and difficult to restore a damaged image by leveraging GA optimization. To address this problem, this paper proposes a novel GA-based …

Lightweight Sharable And Traceable Secure Mobile Health System, Yang Yang, Ximeng Liu, Robert H. Deng, Yingjiu Li

Research Collection School Of Computing and Information Systems

Mobile health (mHealth) has emerged as a new patient centric model which allows real-time collection of patient data via wearable sensors, aggregation and encryption of these data at mobile devices, and then uploading the encrypted data to the cloud for storage and access by healthcare staff and researchers. However, efficient and scalable sharing of encrypted data has been a very challenging problem. In this paper, we propose a Lightweight Sharable and Traceable (LiST) secure mobile health system in which patient data are encrypted end-to-end from a patient’s mobile device to data users. LiST enables efficient keyword search and finegrained access …

Stylized 2d Fabrication Of Non-Photorealistic Images, Athina Panotopoulou

Dartmouth College Ph.D Dissertations

A current trend in computer graphics is the use of programmable tools that allow non-experts to engage in the design of physical prototypes. Within fabrication, one area of research focuses on non-photorealistic images which are stylized to depict a particular aesthetic quality or convey key information. In cases where authenticity is demanded or the images need to be manipulated, fabrication is necessary. Non-photorealistic image fabrication involves two challenges: identifying and abstracting key information during design and considering material restrictions during fabrication. This thesis showcases two examples for fabricating new types of non-photorealistic images, the first involving watercolors, and the second …

Local Binary Pattern Based Algorithms For The Discrimination And Detection Of Crops And Weeds With Similar Morphologies, Vi Nguyen Thanh Le

Theses: Doctorates and Masters

In cultivated agricultural fields, weeds are unwanted species that compete with the crop plants for nutrients, water, sunlight and soil, thus constraining their growth. Applying new real-time weed detection and spraying technologies to agriculture would enhance current farming practices, leading to higher crop yields and lower production costs. Various weed detection methods have been developed for Site-Specific Weed Management (SSWM) aimed at maximising the crop yield through efficient control of weeds. Blanket application of herbicide chemicals is currently the most popular weed eradication practice in weed management and weed invasion. However, the excessive use of herbicides has a detrimental impact …

Using Blockchain For Digital Card Game, Raymond A. Swannack

EWU Masters Thesis Collection

In recent years, the popularity of both online card games and blockchain technology have grown exponentially. While combining these two does not immediately seem like an obvious idea, they in fact complement each other nicely. Blockchain allows for players to actually own their cards, in a way that was unheard of in the digital format just a few years ago. It also gives them the freedom to use them in any way they like, just like in real life. In this thesis we will look into how viable this idea really is. We use the Ethereum virtual machine to simulate …

Using Data Analytics To Filter Insincere Posts From Online Social Networks. A Case Study: Quora Insincere Questions, Mohammad A. Al-Ramahi, Izzat Alsmadi

Computer Information Systems Faculty Publications (Archived)

The internet in general and Online Social Networks (OSNs) in particular continue to play a significant role in our life where information is massively uploaded and exchanged. With such high importance and attention, abuses of such media of communication for different purposes are common. Driven by goals such as marketing and financial gains, some users use OSNs to post their misleading or insincere content. In this context, we utilized a real-world dataset posted by Quora in Kaggle.com to evaluate different mechanisms and algorithms to filter insincere and spam contents. We evaluated different preprocessing and analysis models. Moreover, we analyzed the …

Health Risks Of E-Cigarettes: Analysis Of Twitter Data Using Topic Mining, Abdullah Wahbeh, Mohammad A. Al-Ramahi, Omar El-Gayar, Tareq Nasralah

Computer Information Systems Faculty Publications (Archived)

The recent rise of e-cigarettes and vaping products has increased concerns that another young generation may become addicted to nicotine. Recently, it becomes evident that several health issues are related to the use of e-cigarettes and vaping products. The objective of this paper is to understand and identify such health issues by collecting and analyzing social media data. The analysis reflects the most important themes and topics discussed by online user’s about e-cigarettes, vaping, and associated health issues. Using topic modeling techniques, we were able to identify several health issues related to the use of e-cigarettes and vaping products. These …

Analyze Informant-Based Questionnaire For The Early Diagnosis Of Senile Dementia Using Deep Learning, Fubao Zhu, Xiaonan Li, Daniel Mcgonigle, Haipeng Tang, Zhuo He, Chaoyang Zhang, Guang-Uei Hung, Pai-Yi Chiu, Weihua Zhou

Michigan Tech Publications, Part 1

OBJECTIVE: This paper proposes a multiclass deep learning method for the classification of dementia using an informant-based questionnaire.

METHODS: A deep neural network classification model based on Keras framework is proposed in this paper. To evaluate the advantages of our proposed method, we compared the performance of our model with industry-standard machine learning approaches. We enrolled 6,701 individuals, which were randomly divided into training data sets (6030 participants) and test data sets (671 participants). We evaluated each diagnostic model in the test set using accuracy, precision, recall, and F1-Score.

RESULTS: Compared with the seven conventional machine learning algorithms, the DNN …

Repositories For Taxonomic Data: Where We Are And What Is Missing, Aurélian Miralles, Teddy Bruy, Katherine Wolcott, Mark D. Scherz, Dominik Begerow, Bank Beszteri, Michael Bonkowski, Janine Felden, Birgit Gemeinholzer, Frank Glaw, Frank Oliver Glöckner, Oliver Hawlitschek, Ivaylo Kostadinov, Tim W. Nattkemper, Christian Printzen, Jasmin Renz, Nataliya Rybalka, Marc Stadler, Tanja Weibulat, Thomas Wilke, Susanne S. Renner, Miguel Vences

Harold W. Manter Laboratory of Parasitology: Library Materials

Natural history collections are leading successful large-scale projects of specimen digitization (images, metadata, DNA barcodes), thereby transforming taxonomy into a big data science. Yet, little effort has been directed towards safeguarding and subsequently mobilizing the considerable amount of original data generated during the process of naming 15,000–20,000 species every year. From the perspective of alpha-taxonomists, we provide a review of the properties and diversity of taxonomic data, assess their volume and use, and establish criteria for optimizing data repositories. We surveyed 4,113 alpha-taxonomic studies in representative journals for 2002, 2010, and 2018, and found an increasing yet comparatively limited use …

Designing Shared Control Strategies For Teleoperated Robots Across Intrinsic User Qualities, Nancy Pham

School of Computing: Dissertations, Theses, and Student Research

Accounting for variance in human behavior is an integral part of interacting with robotic systems that share control between users and robots in order to reduce errors, improve performance, and maintain safety. In this work we focus on the shared control of a telepresence robot and how individual user traits may affect a person's performance while navigating the robot. This requires understanding which user qualities impact performance and cause conflicts -- with the ultimate goal of building shared controllers that adapt to those qualities. Toward this goal, we develop novel adaptive shared controllers and integrate the study of intrinsic user …

Comparative Evaluation Of Machine Learning Models For Groundwater Quality Assessment, Shine Bedi, Ashok Samal, Chittaranjan Ray, Daniel D. Snow

School of Computing: Faculty Publications

Contamination from pesticides and nitrate in groundwater is a significant threat to water quality in general and agriculturally intensive regions in particular. Three widely used machine learning models, namely, artificial neural networks (ANN), support vector machines (SVM), and extreme gradient boosting (XGB), were evaluated for their efficacy in predicting contamination levels using sparse data with non-linear relationships. The predictive ability of the models was assessed using a dataset consisting of 303 wells across 12 Midwestern states in the USA. Multiple hydrogeologic, water quality, and land use features were chosen as the independent variables, and classes were based on measured concentration …

Factors That Influence The Perception Of Higher Education Leaders In The Adoption Process Of Instructional Technology And Distance Education, Diego Tibaquirá

Theses and Dissertations

The problem addressed by this study was that many administrators at institutions of higher learning are faced with the task of finding ways to provide the latest technologies while being extremely constricted by budgets and the rising cost of education. The purpose of this study was to determine the factors that influence the perceptions of higher education leaders in the adoption process of Instructional Technology and Distance Education. This included an examination of the decision-making process and what determined if Instructional Technology and Distance Education were either implemented or upgraded at various higher learning institutions.

The researcher implemented a mixed-methods …

Automatic Target Recognition With Deep Metric Learning., Abdelhamid Bouzid

Electronic Theses and Dissertations

An Automatic Target Recognizer (ATR) is a real or near-real time understanding system where its input (images, signals) are obtained from sensors and its output is the detected and recognized target. ATR is an important task in many civilian and military computer vision applications. The used sensors, such as infrared (IR) imagery, enlarge our knowledge of the surrounding environment, especially at night as they provide continuous surveillance. However, ATR based on IR faces major challenges such as meteorological conditions, scale and viewpoint invariance. In this thesis, we propose solutions that are based on Deep Metric Learning (DML). DML is a …

Special Section Guest Editorial: Machine Learning In Optics, Jonathan Howe, Travis Axtell, Khan Iftekharuddin

Electrical & Computer Engineering Faculty Publications

This guest editorial summarizes the Special Section on Machine Learning in Optics.

Polyacrylamide In Glycerol Solutions From An Atomistic Perspective Of The Energetics, Structure, And Dynamics, Scott D. Hopkins, Gideon K. Gogovi, Eric Weisel, Robert A. Handler, Estela Blaisten-Barojas

VMASC Publications

All-atom molecular dynamics is used to investigate the structural, energetic, and dynamical properties of polyacrylamide (PAM) oligomers of different lengths solvated in pure glycerol, a 90:10 glycerol–water mixture, and pure water. We predict that the oligomers’ globular structure is obtained only when the modeling strategy considers the solvent as a continuous background. Meanwhile, for all-atom modeled solvents, the glycerol solutions display a strong tendency of trapping the oligomers in instantaneous elongated random coiled structures that remain locked-in over tens of nanoseconds. In pure water, the oligomers acquire considerably shorter random coiled structures of increased flexibility. The all-atom force field, generalized …

Security In 5g-Enabled Internet Of Things Communication: Issues: Challenges, And Future Research Roadmap, Mohammad Wazid, Ashok Kumar Das, Sachin Shetty, Prosanta Gope, Joel J.P.C. Rodrigues

VMASC Publications

5G mobile communication systems promote the mobile network to not only interconnect people, but also interconnect and control the machine and other devices. 5G-enabled Internet of Things (IoT) communication environment supports a wide-variety of applications, such as remote surgery, self-driving car, virtual reality, flying IoT drones, security and surveillance and many more. These applications help and assist the routine works of the community. In such communication environment, all the devices and users communicate through the Internet. Therefore, this communication agonizes from different types of security and privacy issues. It is also vulnerable to different types of possible attacks (for example, …

The Artificial University: Decision Support For Universities In The Covid-19 Era, Wesley J. Wildman, Saikou Y. Diallo, George Hodulik, Andrew Page, Andreas Tolk, Neha Gondal

VMASC Publications

Operating universities under pandemic conditions is a complex undertaking. The Artificial University (TAU) responds to this need. TAU is a configurable, open-source computer simulation of a university using a contact network based on publicly available information about university classes, residences, and activities. This study evaluates health outcomes for an array of interventions and testing protocols in an artificial university of 6,500 students, faculty, and staff. Findings suggest that physical distancing and centralized contact tracing are most effective at reducing infections, but there is a tipping point for compliance below which physical distancing is less effective. If student compliance is anything …