Computer Sciences Commons^™

Perceptions, Potholes, And Possibilities Of Using Digital Voice Assistants To Differentiate Instructions, Adrian A. Weir

Walden Dissertations and Doctoral Studies

Access to technologies and understanding the potential uses of technology to differentiate instruction have been a concern for the teachers and students in a local school district located in the southeastern United States. Despite the emergence of digital voice assistants (DVAs) as tools for instructions, teachers lack knowledge and strategies for using DVAs to differentiate instruction in their classrooms. The purpose of this qualitative study was to identify teacher knowledge and strategies employed among special education (SPED) teachers using DVAs to differentiate instruction in their classrooms. The concepts of Carol Tomlinson’s differentiation theory and Mishra and Koehler’s TPACK framework served …

Text Mining Methods For Analyzing Online Health Information And Communication, Sifei Han

Theses and Dissertations--Computer Science

The Internet provides an alternative way to share health information. Specifically, social network systems such as Twitter, Facebook, Reddit, and disease specific online support forums are increasingly being used to share information on health related topics. This could be in the form of personal health information disclosure to seek suggestions or answering other patients' questions based on their history. This social media uptake gives a new angle to improve the current health communication landscape with consumer generated content from social platforms. With these online modes of communication, health providers can offer more immediate support to the people seeking advice. Non-profit …

“Distance Learning” In The Ninth Century?: Micro-Cluster Analysis Of The Epistolary Network Of Alcuin After 796, William James Mattingly

Theses and Dissertations--History

Scholars of eighth- and ninth-century education have assumed that intellectuals did not write works of Scriptural interpretation until that intellectual had a firm foundation in the seven liberal arts.This ensured that anyone who embarked on work of Scriptural interpretation would have the required knowledge and methods to read and interpret Scripture correctly. The potential for theological error and the transmission of those errors was too great unless the interpreter had the requisite training. This dissertation employs computistical methods, specifically the techniques of social network mapping and cluster analysis, to study closely the correspondence of Alcuin, a late-eighth- and early-ninth-century scholar …

Orthogonal Recurrent Neural Networks And Batch Normalization In Deep Neural Networks, Kyle Eric Helfrich

Theses and Dissertations--Mathematics

Despite the recent success of various machine learning techniques, there are still numerous obstacles that must be overcome. One obstacle is known as the vanishing/exploding gradient problem. This problem refers to gradients that either become zero or unbounded. This is a well known problem that commonly occurs in Recurrent Neural Networks (RNNs). In this work we describe how this problem can be mitigated, establish three different architectures that are designed to avoid this issue, and derive update schemes for each architecture. Another portion of this work focuses on the often used technique of batch normalization. Although found to be successful …

Design Of A Remote Real-Time Groundwater Level And Water Quality Monitoring System For The Philippine Groundwater Management Plan Project, Carlos M. Oppus, Ma. Aileen Leah G. Guzman, Maria Leonora Guico, Jose Claro N. Monje, Mark Glenn F. Retirado, John Chris T. Kwong, Genevieve C. Ngo, Annael J. Domingo

Electronics, Computer, and Communications Engineering Faculty Publications

Recent technological advances allow us to utilize remote monitoring systems or real-time access of data. While the use of remote monitoring systems is not new, there are still numerous applications that can be explored and improved on, one such is groundwater level and quality monitoring. In the Philippines, the extraction of groundwater for both domestic use and industrial use are manually monitored by the government’s concerned agency and is done at least once per year. With this current setup, the real and significant state of the groundwater is not reflected in a way that is most valuable to the government …

Endpoints And Interdependencies In Internet Of Things Residual Artifacts: Measurements, Analyses, And Insights Into Defenses, Jinchun Choi

Electronic Theses and Dissertations, 2020-2023

The usage of Internet of Things (IoT) devices is growing fast. Moreover, the lack of security measures among the IoT devices and their persistent online connection give adversaries an opportunity to exploit them for multiple types of attacks, such as distributed denial-of-service (DDoS). To understand the risks of IoT devices, we analyze IoT malware from an endpoint standpoint. We investigate the relationship between endpoints infected and attacked by IoT malware, and gain insights into the underlying dynamics in the malware ecosystem. We observe the affinities and different patterns among endpoints. Towards this, we reverse-engineer 2,423 IoT malware samples and extract …

The Susceptibility Of Deep Neural Networks To Natural Perturbations, Mesut Ozdag

Electronic Theses and Dissertations, 2020-2023

Deep learning systems have achieved great success in various types of applications in recent years. They are increasingly being adopted for safety-critical tasks such as face recognition, surveillance systems, speech recognition, and autonomous driving. On the other hand, it has been found that deep neural networks (DNNs) can easily be fooled by adversarial input samples. These imperceptible perturbations on images can lead any machine learning system to misclassify the objects with high confidence. Furthermore, they can be almost indistinguishable to a human observer. These systems can also be exposed to adverse weather conditions such as fog, rain, and snow. This …

Towards Large-Scale And Robust Code Authorship Identification With Deep Feature Learning, Mohammed Abuhamad

Electronic Theses and Dissertations, 2020-2023

Successful software authorship identification has both software forensics applications and privacy implications. However, the process requires an efficient extraction of quality authorship attributes. The extraction of such attributes is very challenging due to several factors such as the variety of software formats, number of available samples, and possible obfuscation or adversarial manipulation. We focus on software authorship identification from three central perspectives: large-scale single-authored software, real-world multi-authored software, and the robustness assessment of code authorship identification methods against adversarial attacks. First, we propose DL-CAIS, a deep Learning-based approach for software authorship attribution, that facilitates large-scale, format-independent, language-oblivious, and obfuscation-resilient software …

Improving Security Of Crypto Wallets In Blockchain Technologies, Hossein Rezaeighaleh

Electronic Theses and Dissertations, 2020-2023

A big challenge in blockchain and cryptocurrency is securing the private key from potential hackers. Nobody can rollback a transaction made with a stolen key once the network confirms it. The technical solution to protect private keys is the cryptocurrency wallet, software, hardware, or a combination to manage the keys. In this dissertation, we try to investigate the significant challenges in existing cryptocurrency wallets and propose innovative solutions. Firstly, almost all cryptocurrency wallets suffer from the lack of a secure and convenient backup and recovery process. We offer a new cryptographic scheme to securely back up a hardware wallet relying …

Multi-Agent Reinforcement Learning For Defensive Escort Teams, Hassam Sheikh

Electronic Theses and Dissertations, 2020-2023

Reinforcement learning has been applied to solve several real world challenging problems, from robotics to data center cooling. Similarly, adaption of reinforcement learning for multi-agent systems facilitated applications such as optimal multi-robot control and analysis of social-dilemmas. In this dissertation, we show that multi-agent reinforcement learning algorithms suffer from several stability issues such as multi-scenario learning, unstable training in dual-reward setting, overestimation bias and value function collapse, and provide solutions to each of these problems respectively. Several contributions of this dissertation have been formalized within the framework of a defensive escort team problems, a scenario where a team of learning …

Attack Detection And Mitigation In Mobile Robot Formations, Arnold Fernandes

Masters Theses

"A formation of cheap and agile robots can be deployed for space, mining, patrolling, search and rescue applications due to reduced system and mission cost, redundancy, improved system accuracy, reconfigurability, and structural flexibility. However, the performance of the formation can be altered by an adversary. Therefore, this thesis investigates the effect of adversarial inputs or attacks on a nonholonomic leader-follower-based robot formation and introduces novel detection and mitigation schemes.

First, an observer is designed for each robot in the formation in order to estimate its state vector and to compute the control law. Based on the healthy operation of the …

Coverage Guided Differential Adversarial Testing Of Deep Learning Systems, Jianmin Guo, Houbing Song, Yue Zhao, Yu Jiang

Publications

Deep learning is increasingly applied to safety-critical application domains such as autonomous cars and medical devices. It is of significant importance to ensure their reliability and robustness. In this paper, we propose DLFuzz, the coverage guided differential adversarial testing framework to guide deep learing systems exposing incorrect behaviors. DLFuzz keeps minutely mutating the input to maximize the neuron coverage and the prediction difference between the original input and the mutated input, without manual labeling effort or cross-referencing oracles from other systems with the same functionality. We also design multiple novel strategies for neuron selection to improve the neuron coverage. The …

Recurrent Neural Network Properties And Their Verification With Monte Carlo Techniques, Dmitry Vengertsev, Elena Sherman

Computer Science Faculty Publications and Presentations

As RNNs find its applications in medical and automotive fields, they became a part of critical systems, which traditionally require thorough verification processes. In this work we present how RNNs behaviors can be modeled as labeled transition systems and formally define a set of state and temporal safety properties for such models. To verify those properties we propose to use the Monte Carlo approach and evaluate its effectiveness for different type of properties. We perform empirical evaluation on two RNN models to determine to what extent they satisfy the properties and how many the samples of Monte Carlo required to …

What Snippets Feel: Depression, Search, And Snippets, Ashlee Milton, Maria Soledad Pera

Computer Science Faculty Publications and Presentations

Mental health disorders (MHD) is a rising, yet stigmatized, topic in the United States. Individuals suffering from MHD are slowing starting to overcome this stigma by discussing how technology affects them. Researchers have explored behavioral nuances that emerge from interactions of individuals affected by MHD with persuasive technologies, mainly social media. Yet, there is a gap in the analysis pertaining to search engines, another persuasive technology, which is part of their everyday lives. In this paper, we report the results of an initial exploratory analysis conducted to understand the sentiment/emotion profiles of search engines handling the information needs of searchers …

Say It With Emojis: Co-Designing Relevance Cues For Searching In The Classroom, Mohammad Aliannejadi, Monica Landoni, Theo Huibers, Emiliana Murgia, Maria Soledad Pera

Computer Science Faculty Publications and Presentations

Search Engine Result Pages (SERP) include snippets of retrieved resources as a means to help searchers select the ones that satisfy their information needs. This way, result relevance can be determined by scanning through snippets, an exercise that requires experience with reading, understanding, and assessing the value of a document. These are skills that primary school children are still developing and thus are not yet proficient with. As web search tools are essential to support children learning at school and home, we explore how to help young searchers in making informed relevance assessments while conducting searches in a classroom. In …

Prediction Of Fatality Crashes With Multilayer Perceptron Of Crash Record Information System Datasets, Thanh Hung Duong, Fengxiang Qiao, Jyh-Haw Yeh, Yunpeng Zhang

Computer Science Faculty Publications and Presentations

Despite the effort of the authorities and researchers, there has been no sign of decreasing in the number of fatal crashes annually. To analyze the deadly collisions, researchers have focused on finding which factors affect injury severity, and thus many crash prediction models for it had been developed. Commonly the injury severity is categorized into five different classes. Still, in many studies, minority classes like fatality and incapacitating injury were merged so that the dataset becomes balanced, and the model can provide decent predictions. However, this approach does not help analyze the fatal crashes as they are joined with other …

Leading Through Change: 2020, Domenick Pinto

School of Computer Science & Engineering Faculty Publications

Having served as department chair and school director for 31 years, I have witnessed a tremendous evolution in the role of chair as economic, social and student climates have changed. My session will summarize collected data from chairs of departments of various sizes and types in order to discuss and understand better our ever changing role as we see responsibilities of delegating, leading change, creative budgeting and fundraising, grant writing and managing conflict become vital to our positions.

Introducing Parallelism To First-Year Cs Majors, Barbara M. Anthony, D. Cenk Erdil, Olga Glebova, Robert Montante

School of Computer Science & Engineering Faculty Publications

We propose to strengthen the computer science (CS) curriculum by embedding parallel concepts in a required first-semester seminar taken by all incoming declared CS majors. We introduce students to parallel computing concepts through a series of unplugged activities so that students see parallel approaches as a natural form of solution to a task. We describe a pilot offering of the class and activities, with measurements and analysis of what students self-report and their performance on assessments.

On Using Model For Downstream Responsibility, Frances S. Grodzinsky, Marty J. Wolf, Keith W. Miller

School of Computer Science & Engineering Faculty Publications

The authors identify features of software and the software development process that may contribute to the differences in the level of responsibility assigned to the software developers when they make their software available for others to use as a tool in building a second piece of software. They call this second use of the software "downstream use."

Zhvillimi I Time Attendance Sistemit Të Bazuar Në Arduino, Mërgim Alidema

Theses and Dissertations

Procesi i përcjellës së kohës se kur është filluar puna dhe kohëzgjatjes së saj ka qenë një çështje e komplikuar. Për shumë vite shumë metoda janë zhvilluar për të monitoruar prezencën e punëtoreve në vendin e tyre të punës. Më të popullarizuarat kanë qenë karta kohore dhe pasqyra e mungesave.

Secila nga këto opsione ka disavantazhët e veta. Në dekadën e fundit shumë kompani kanë filluar të integrojnë softuerin dhe pajisje moderne për regjistrimin e hyrje-daljeve që të monitorojnë prezencën e punëtoreve të tyre. Këto sistem përveç që ofrojnë regjistrimin e hyrje-daljeve janë në gjendje edhe të gjenerojnë raporte ditore, …

Needfinding, Devorah Kletenik

Open Educational Resources

This activity guides students through the process needfinding to identify areas of need for their creation of a technology for the "public good." Students will conduct contextual inquiry to identify the needs of their target audience.

Personas, Scenarios And Storyboards, Devorah Kletenik

Open Educational Resources

This activity guides students towards the creation of personas, scenarios and storyboards for a product/website that they are creating.

Public Interest Technology: Coding For The Public Good, Devorah Kletenik

Open Educational Resources

These slides are used to guide a discussion with students introducing them to the notion of public interest technology and coding for the public good. The lesson is intended to spark a discussion with students about different sorts of technology and their societal ramifications.

Accessibility: The Whys And The Hows, Devorah Kletenik

Open Educational Resources

This presentation introduces Computer Science students to the notion of accessibility: developing software for people with disabilities. This lesson provides a discussion of why accessibility is important (including the legal, societal and ethical benefits) as well as an overview of different types of impairments (visual, auditory, motor, neurological/cognitive) and how developers can make their software accessible to users with those disabilities. This lesson includes videos and links to readings and tutorials for students.

Coding For The Public Good: Front-End Website Design And Development, Devorah Kletenik

Open Educational Resources

This activity helps student design and develop a front-end of a website, from wireframes through HTML/CSS/Javascript. It includes design questions for students, including the invocation of Ben Schneiderman's eight golden rules for interface design.

Note: this activity assumes prior knowledge of web development. Since this activity is designed for an HCI course, with a focus on interface design, students are not expected to create a back-end for it. This activity can obviously be modified for a full-stack experience.

Accessibility Evaluation, Devorah Kletenik

Open Educational Resources

This activity guides students through the evaluation of a website that they have created to see if it is accessible for users with disabilities. Students will simulate a number of different disabilities (e.g. visual impairments, color blindness, auditory impairments, motor impairments) to see if their website is accessible; they will also use automated W3 and WAVE tools to evaluate their sites. Students will consider the needs of users with disabilities by creating a persona and scenario of a user with disabilities interacting with their site. Finally, students will write up recommendations to change their site and implement the changes.

Structural And Lexical Methods For Auditing Biomedical Terminologies, Rashmie Abeysinghe

Theses and Dissertations--Computer Science

Biomedical terminologies serve as knowledge sources for a wide variety of biomedical applications including information extraction and retrieval, data integration and management, and decision support. Quality issues of biomedical terminologies, if not addressed, could affect all downstream applications that use them as knowledge sources. Therefore, Terminology Quality Assurance (TQA) has become an integral part of the terminology management lifecycle. However, identification of potential quality issues is challenging due to the ever-growing size and complexity of biomedical terminologies. It is time-consuming and labor-intensive to manually audit them and hence, automated TQA methods are highly desirable. In this dissertation, systematic and scalable …

Estimating Free-Flow Speed With Lidar And Overhead Imagery, Armin Hadzic

Theses and Dissertations--Computer Science

Understanding free-flow speed is fundamental to transportation engineering in order to improve traffic flow, control, and planning. The free-flow speed of a road segment is the average speed of automobiles unaffected by traffic congestion or delay. Collecting speed data across a state is both expensive and time consuming. Some approaches have been presented to estimate speed using geometric road features for certain types of roads in limited environments. However, estimating speed at state scale for varying landscapes, environments, and road qualities has been relegated to manual engineering and expensive sensor networks. This thesis proposes an automated approach for estimating free-flow …

Deep Neural Architectures For End-To-End Relation Extraction, Tung Tran

Theses and Dissertations--Computer Science

The rapid pace of scientific and technological advancements has led to a meteoric growth in knowledge, as evidenced by a sharp increase in the number of scholarly publications in recent years. PubMed, for example, archives more than 30 million biomedical articles across various domains and covers a wide range of topics including medicine, pharmacy, biology, and healthcare. Social media and digital journalism have similarly experienced their own accelerated growth in the age of big data. Hence, there is a compelling need for ways to organize and distill the vast, fragmented body of information (often unstructured in the form of natural …

Temporal Data Extraction And Query System For Epilepsy Signal Analysis, Yan Huang

Theses and Dissertations--Computer Science

The 2016 Epilepsy Innovation Institute (Ei2) community survey reported that unpredictability is the most challenging aspect of seizure management. Effective and precise detection, prediction, and localization of epileptic seizures is a fundamental computational challenge. Utilizing epilepsy data from multiple epilepsy monitoring units can enhance the quantity and diversity of datasets, which can lead to more robust epilepsy data analysis tools. The contributions of this dissertation are two-fold. One is the implementation of a temporal query for epilepsy data; the other is the machine learning approach for seizure detection, seizure prediction, and seizure localization. The three key components of our temporal …