Master of Arts (MA), Ohio University, 2014, Film (Fine Arts)

This thesis explores surveillance cinema though a body of films that thematize the increasing presence of surveillance images in social life. Suggesting a shift from two dominant representational modes, dystopic and conspiratorial cinema, recent surveillance films normalize the practice, diffuse the technology's paranoiac connotation, and transcend the rhetoric that surveillance scenarios only target white men formerly a part of the surveillance apparatus. I argue that with new forms of the image, through cinema's figuration of the surveillance image, “othered” subjects appear as “to-be-surveilled”—targeted bodies due to the determinants of their gender, sexuality, race, ethnicity, or class status. The films I discuss thus raise an important question relevant to contemporary surveillance theory: does cinema normalize forms of control or does it challenge these structures through a critique of the power relations found within them? As surveillance cinema integrates images of surveillance into its texts, it critiques the power disparities that exist between hegemonic disembodied surveillors and controlled bodies.

Master of Arts (MA), Bowling Green State University, 2016, Media and Communication

This study applied the uses and gratifications (U&G) perspective in order to explore Chinese students' SNS (Social Networking Site) identity construction in four ways: (1) how Chinese young adults studying in the U.S. use various kinds of SNSs, (2) how their use of SNSs are influenced by the surveillance of the Chinese government, (3) how their experience with and perceived threat of surveillance varies depending on the type of SNS being used, and (4) how their experience with and perceived threat of surveillance are related to their SNS posting behaviors and identify construction on SNSs. This study categorized SNSs by their national origin (Chinese SNSs vs. U.S. SNSs) and by their network openness (open SNSs vs. closed SNSs). Thus, SNSs were assigned to one of the four categories: (1) Chinese open SNSs, (2) Chinese closed SNSs, (3) U.S. open SNSs, and (4) U.S. closed SNSs. 169 Chinese students attending colleges in the U.S. participated in a survey for this study. They were asked about their experience with and perceived threat of surveillance, posting behaviors, and identify construction on the four different types of SNSs. This study found that Chinese students in the U.S. have different experiences and perceptions of surveillance depending on the type of SNS they use. This study also found that the different level of surveillance experience and perceived threat of surveillance were related to different SNS posting behaviors and identity construction strategies. Implications of these findings are discussed, and limitations and opportunities for future research are addressed.

Master of Engineering, Case Western Reserve University, 2013, EECS - Computer Engineering

Some buildings require high level of surveillance and do not expect visitors after working hours. This thesis study deals in making a smarter application for Android device, which makes surveillance system more intelligent and handy. This application can be used in two different ways: 1- It can be used to replace TV setup connecting all network cameras, as security officer can connect this device with any camera to stream live video. 2- It can detect motion in camera and trigger notification to security officer with live video from that camera, providing him with runtime activities and position of an intruder while chasing him.

MS, Kent State University, 2019, College of Arts and Sciences / Department of Computer Science

Cameras are rapidly becoming a daily part of our lives. Their constant streaming of information about people gives rise to different security and privacy concerns. Human analysis using cameras or surveillance footage is an active field of research. Different methods have been introduced which showed success in both the detection and tracking of pedestrians. Once a human is detected and/or tracked, different motion analyses can be performed in order to better understand and model human behavior. A majority of these methods do not take user privacy or security into account, making security monitoring systems a significant threat to individuals' privacy. This threat becomes more serious and evident when the security cameras are installed in places where vulnerable people (e.g. elders, children) frequently spend time such as day-cares, schools, retirement homes, or violated to serve independent interests. This work presents a model that is able to understand human motion, and deploys an anonymization technique that facilitates the preservation of an individual's privacy and security. Along with this, a new video dataset called PAVD, Privacy Aware Video Dataset, is introduced that proposes a privacy aware approach of representing motion in video datasets for learning purposes.

Master of Arts in Rhetoric and Writing​, University of Findlay, 2017, English

Since the advent of digital composing methods, scholars of first-year writing have produced research exploring the implications of digital writing instruction for writing professionals and students. However, despite extensive consideration of how digital writing instruction may perpetuate societal inequalities, little scholarship has explored how the government's digital surveillance of citizens may jeopardize writing studies' understanding of human agency and its mission to preserve student agency even as students interact with increasingly complex, networked digital interfaces. In the following thesis, I address this gap by examining available information regarding the NSA's surveillance of web users and the role web companies such as Microsoft play in such surveillance. Furthermore, I review composition studies scholarship that examines the implications of the digital interface for writing instruction, scholarship that has recently grown concerned with the potential for the government to exploit networked digital interfaces as a means of surveilling users. I suggest that Cynthia Selfe's argument to writing professionals to pay attention to their technology use reinscribes a democratic humanist vision of agency. Furthermore, I suggest that the correlation of paying attention with increased agency limits scholars' understanding of the insidious, secretive nature of government surveillance as an alien object that resists understanding. Ultimately, I present alien phenomenology as an alternative theoretical lens through which scholars may pay attention to government surveillance without assuming that doing so will increase the agency of writing professionals or students. Finally, I suggest that by paying attention to government surveillance through the lens of alien phenomenology, scholars may consider the possibility that agency is not a sustainable category as writing professionals and students engage with networked digital interfaces implicated in government surve (open full item for complete abstract)

Doctor of Philosophy (Ph.D.), Bowling Green State University, 2015, English (Rhetoric and Writing) PhD

Each time our students and colleagues participate online, they face invisible tracking technologies that harvest metadata for web customization, targeted advertising, and even state surveillance activities. This practice may be of concern for computers and writing and digital humanities specialists, who examine the ideological forces in computer-mediated writing spaces to address power inequality, as well as the role ideology plays in shaping human consciousness. However, the materiality of technology—the non-human objects that surrounds us—is of concern to those within rhetoric and composition as well. This project shifts attention to the materiality of non-human objects, specifically computer algorithms and computer code. I argue that these technologies are powerful non-human objects that have rhetorical agency and persuasive abilities, and as a result shape the everyday practices and behaviors of writers/composers on the web as well as other non-human objects. Through rhetorical inquiry, I examine literature from rhetoric and composition, surveillance studies, media and software studies, sociology, anthropology, and philosophy. I offer a “built ecological network” theory that is the manufactured and natural rhetorical rhizomatic network full of spatial, material, social, linguistic, and dynamic energy layers that enter into reflexive and reciprocal relations to support my claim that computer algorithms have agency and persuasive abilities. I also address how computer code figures in digital surveillance environments on the web, as well as how to refigure digital rhetoric and literate practices through the built ecological network. My results help shift attention to the role rhetoric plays in materiality, and further implicates rhetoric as both under the realm of human and material activity.

Master of Science, The Ohio State University, 2011, Computer Science and Engineering

In this work, we provide tracking and activity analysis of an aerial video data set. We propose algorithms that are both scalable and able to handle the additional challenges presented by this form of data. Specifically, we present a method that consists of two main parts: track extraction and traffic activity analysis. After a preprocessing stabilization step, we use a constrained interest point matching algorithm to generate tracking data of vehicles in the scene. Finally, we present algorithms that use this data to recognize traffic activity patterns such as traffic direction, bidirectional roads, bidirectional stops, and accelerations/ decelerations via analysis of average speed patterns. We provide a thorough analysis of our results, including quantitative analysis of our activity analysis algorithms.

Doctor of Philosophy, The Ohio State University, 2007, Geography

This dissertation analyzes crime in both ecological and surveillance perspectives. In ecological perspective, many studies ignore spatial effects in the models, leading to inefficient and biased results. This dissertation, by applying Bayesian hierarchical analysis, accounts for spatial effect in the model and presents correct socio-demographic factors related to residential crime occurrences. In surveillance perspective, literature to date has limitations in presenting exact locations of crime hotspots and implementing continuous analysis over time. Use of population information is the main reason of the limitations in the literature. Because the population information is based on census administrative area unit and is only updated in decennial bases, corresponding hotspots involve approximations in both population size and their locations. However, this study handles the problem by applying a newly devised surveillance method, which uses only crime accounts over time without the use of population information. The goal of this dissertation is providing significant demographic factors of crime and crime hotspots in near real time base, which will contribute to crime control. This goal is achieved by 1) handling spatial autocorrelation and heterogeneity in the analysis, 2) visualizing spatial effects on a map, 3) enabling continuous surveillance over time, 4) providing precise crime hotspot locations, and 5) presenting local changes in clusters over time. The models presented in this dissertation is applied to residential crimes occurred in Columbus, Ohio for the year 2000. Empirical results present significant demographic factors of residential crimes and locations of crime hotspots over time in near real-time framework.

Doctor of Philosophy, The Ohio State University, 2007, Veterinary Preventive Medicine

Emerging infectious diseases and the threat of bioterrorism have fostered a desire for improved timeliness of outbreak detection. Traditional disease reporting is reliant on confirmed diagnoses, often involving laboratory analysis that may require days to complete. Most emerging infectious and bioweapon pathogens are zoonotic organisms. Detection of zoonotic outbreaks has often relied on the identification of human cases. We investigated how data from veterinary diagnostic laboratories (VDLs) might contribute to earlier outbreak detection efforts in Ohio. We began by determining the representation of animal species in the data and evaluating the representation of human households. Companion animals comprised 98.1% of the total number of specimens submitted to a commercial, nation-wide VDL from clinics in Ohio in one year. Using estimates derived from a survey of pet owners, we determined that these data represented approximately 6.6% of Ohio households. The value of microbiology test orders was determined by quantifying the representation and potential gain in timeliness from two VDL datasets. We also investigated the potential to determine estimated count values from historical records and detect significant increases in these values using statistical-based detection methods. The data represented specimens from mostly companion animals (85.0% and 74.3%) followed by horses (8.2% and 17.2%). We determined a potential gain of timeliness in outbreak detection of three to five days. We developed baselines of microorganism incidence and total microbiology orders from the datasets and detected some of the clusters of pathogen-specific isolates by analyzing the weekly totals of all microbiology orders. We demonstrated how someone might use these data in a prospective system to detect outbreaks of disease earlier than traditional methods. Case reviews from a pilot system indicated the potential benefit to public health as well as veterinary community. We concluded from thes (open full item for complete abstract)

Master of Science, The Ohio State University, 2006, Graduate School

Doctor of Philosophy, The Ohio State University, 2024, Plant Pathology

Often overshadowed by staple crops, vegetables contain many essential vitamins and minerals and play a key role in global food and nutritional security. However, vegetable production is threatened by a variety of diseases, including bacterial wilt and bacterial spot. My dissertation research utilizes genomic surveys to elucidate the diversity of the bacteria causing these diseases, both globally and locally. Bacterial wilt disease is endemic within the country of Cambodia, causing significant yield losses for Cambodian growers. However, the diversity of the Ralstonia solanacearum Species Complex (RSSC), the causal agent responsible for bacterial wilt disease, has not been defined in Cambodia. Therefore, we conducted a bacterial wilt survey within Cambodia, collecting RSSC isolates from four distinct host plants (tomato, hot pepper, long bean, and bitter gourd) over three locations, for a total of 24 RSSC isolates. We found that all 24 of the Cambodian RSSC isolates belong to phylotype I and are classified as Ralstonia pseudosolanacearum. Through disease progress assays on susceptible hosts, we observed that variation in the Cambodian isolate's ability to cause consistent wilt was dependent on the method of inoculation. Additionally, the Cambodian R. pseudosolanacearum isolates exhibited a wide range of phylogenomic diversity. When comparing the core and accessory genome and the Type III effector profile of the Cambodian isolates, we found that the R. pseudosolanacearum accessory genome better reflected the host of isolation and host range of the isolates compared to the core genome. Altogether, this research provides a glimpse into the RSSC diversity present within Cambodia and insight into R. pseudosolanacearum host range. Bacterial spot disease affects tomato and pepper production worldwide and is caused by a species complex of Xanthomonas bacteria: X. hortorum pv. gardneri, X. euvesicatoria pv. euvesicatoria, X. euvesicatoria pv. perforans, and X. vesicatoria. We (open full item for complete abstract)

Master of Science in Criminal Justice, Youngstown State University, 2023, Department of Criminal Justice and Consumer Sciences

The Uniting and Strengthening America by Providing Appropriate Tools Required to Intercept and Obstruct Terrorism Act, more widely known as the USA PATRIOT Act of 2001, is a groundbreaking piece of legislation that was swiftly enacted in response to the terrorist attacks against the World Trade Center and the Pentagon on September 11, 2001. The USA PATRIOT Act of 2001 consists of ten different sections of text thoroughly detailing redesigned governmental functions, all of which generally aim to prevent, mitigate, and eliminate the threat that terrorism poses against the United States and its citizens. The second section, known as Title II: Enhanced Surveillance Procedures, expanded federal law enforcement's authority to conduct more thorough surveillance of terrorist activity. This thesis is guided by the following research question: How has the USA PATRIOT Act of 2001 impacted the way that federal law enforcement conducts the surveillance of terrorist activity in the United States? For this thesis, the methodology and design consists of an explanatory, single-case study which investigates and analyzes Title II of the USA PATRIOT Act of 2001 within the context of surveillance counterterrorism measures implemented by federal law enforcement in the United States. This thesis builds upon preexisting counterterrorism literature and is beneficial to future studies which attempt to thwart the perpetual fight against terrorism and strengthen national defense against foreign and domestic enemies.

Doctor of Philosophy, The Ohio State University, 2023, Biomedical Sciences

This dissertation introduces the Tool-Assisted Research Discovery Informatics Surveillance System (TARDISS), a proof of concept of a research surveillance system (RSS). TARDISS aims to reduce the transaction costs of Knowledge Production by using templated analysis and automated data management, which allows for studies built using TARDISS to update when new data are released. TARDISS focuses on research that uses the 2012-2020 Centers for Medicare & Medicaid Services (CMS) Standard Analytical File (SAF) Claims datasets and aims to increase the velocity of information sharing by facilitating A/B comparisons between original and updated studies. By automatically updating studies, TARDISS can help identify when the findings of historical scholarship are no longer consistent with the present reality by helping researchers identify trends and discontinuities in large secondary datasets. The research employed a mixed-methods approach with quantitative analysis of literature replications identified by an environmental scan to validate that TARDISS functions as an RSS and qualitative interviews and user story mapping to understand researchers that might benefit from TARDISS and how they perceived the usefulness and useability of TARDISS. This dissertation also explores barriers to adopting research surveillance, including lack of code transparency, the low priority of replication research, and user adoption.

Master of Science (MS), Wright State University, 2023, Biochemistry and Molecular Biology

Current surveillance focuses on well characterized pathogens such as influenza. Since 2000, there have been multiple outbreaks of respiratory disease. These outbreaks have demonstrated the need for robust and collaborative global efforts to identify, monitor, and contain novel respiratory viruses. This study aims to improve the ability of public health agencies to monitor and respond to respiratory disease outbreaks. Using five respiratory pathogens, this study compares a molecular capture technology from Twist Biosciences to the shotgun sequencing approach of whole transcriptome amplification (WTA, Qiagen) with the goal of determining which method is most effective using cost, usability, and sequencing quality metrics for evaluation. Twist, though more expensive, had a 92.1% positive identification of targets on successful sequencing runs with greater depth and breadth of coverage. WTA failed to sequence and identify targets except Human Adenovirus 7, proving that Twist is more reliable and efficient in this study.

PHD, Kent State University, 2022, College of Education, Health and Human Services / School of Foundations, Leadership and Administration

In 2015, then-Higher Education Minister Rodolfo Alarcon said in response to many Cubans' inability to communicate in the international language, English fluency would be required by students as a university exit requirement (“Mastering English,” 2015). The purpose of this interpretive qualitative study was to understand the experiences of Cuban university English instructors, who encountered these curricular changes. The data were collected using semi-structured interviews with six university instructors and emailed responses from another six. The participants included both current and former faculty members who left the teaching profession for more lucrative careers in private tutoring or tourism. Motivational Systems Theory (Ford, 1992), various aspects of social identity (Gray & Morton, 2018) and my own experience as an EFL instructor guided the study in understanding the instructors' social identity, motivation, and self-agency. The data were analyzed using computer-assisted qualitative data analysis software. The findings showed differing experiences among men and women and urban and rural instructors. Faculty expressed both positive and negative teaching experiences, and, as a result of some unfavorable experiences, some instructors left the field of teaching altogether and applied their talents to the growing tourism and private business sectors. In addition to understanding the experiences of the study's participants, the investigation also provides valuable insight into the evolution of English teaching in Cuban higher education, the consequences of educational borrowing and the complexity of conducting research within an authoritarian regime.

Master of Science (M.S.), University of Dayton, 2022, Electrical and Computer Engineering

Remote sensing has seen significant attention due to advances in technology and access to data. A current challenge is classifying land regions by their usage – residential, industrial, forest, etc. Scope is very important, too large of an area would lead to multiple classes being present in one scene, and too small of an area would not contain enough contextual information to accurately determine a scene. To further complicate matters, there are multiple similar objects all present in different classes, for example trees are found in residential, forest, and park classes. Deep learning is a current technology that is successful with problems at this level of ambiguity. The most straight-forward approach to address this level of complexity is to use remote sensing images to classify land regions. However, deep learning using 2D images has its downsides, especially when analyzing aerial data, namely, it lacks 3-dimensional information such as depth. Similarly, there are also 3D deep learning architectures with different weaknesses, i.e., longer processing times and lack of intensity information. As access to processing hardware and remote sensing data continues to increase, there is a pressing need to leverage the strengths of both modalities. This can be done in one of three ways: (1) a data-level fusion, where data modalities are fused together directly; (2) a feature-level fusion, where features are fused after data modalities are processed individually; or (3) a decision-level fusion, where predictions are made using each modality independently, until, ultimately, they are fused into one final decision. In this work, we utilize a feature-level fusion because our dataset (comprised of lidar and RGB scenes) have very different types of information; after analysis, we found that each modality was better suited to different sections of our data, which we could harness using a feature-level fusion. Furthermore, to improve on these results, an accurate regist (open full item for complete abstract)

Master of Science, The Ohio State University, 2022, Environmental Science

In December of 2019, an outbreak of a pneumonia-like virus was identified; this virus, later identified as SARS-CoV-2, eventually became the cause of the COVID-19 pandemic. The COVID-19 pandemic effectively transformed how the world acted and has continued to challenge public health and scientific communities. As the pandemic evolved, the pressure for a return to normalcy was increasingly present, and with that was a pressure to adapt safety precautions to allow for a safe reopening of the world. One concern of the pandemic was students relocating from around the globe and congregating on university campuses in dormitories. With the constant relocating nature of many students, an imperative part of pandemic mitigation has been instilling university health and safety protocols. This thesis will focus on two components of those protocols: wastewater-based surveillance and vaccination. The first chapter of this thesis is a literature review that introduces SARS-CoV-2 and provides a brief history on the development of the pandemic as well as how surveillance and vaccinations were evolved for COVID-19 mitigation efforts. It provides insight into the evolving role of wastewater in research and surveillance, as well as how it has become integrated into the pandemic mitigation systems. The chapter also delves into a brief history on vaccine hesitancy, and how vaccines as well as the stigma surrounding them have altered predicted timelines of the pandemic. The second chapter discusses the dormitory SARS-CoV-2 wastewater surveillance program instilled at The Ohio State University as a complementary tool to the university-wide COVID-19 saliva testing program. The main objectives were to provide rapid SARS-CoV-2 wastewater trend tracking data, analyze relationships between case numbers and wastewater signals on campus, analyze the effect of normalization with human fecal viral indicator concentrations in wastewater, and investigating the relationship of the viral gene copie (open full item for complete abstract)

PHD, Kent State University, 2021, College of Arts and Sciences / Department of History

“What We Got to Say” examines a political period in hip-hop history during the late 1980s and early 1990s. This dissertation was partly inspired by contemporaneous examples of systemic oppression inside the criminal justice system as well as racial hostility that developed out of a series of police officer-involved shootings. It was in large part inspired by an intellectual curiosity to explore the connection between the failures of the modern civil rights movement and the politization of hip-hop. It argues that a hip-hop social movement emerged in this period to protest Ronald Reagan's expansion of the criminal justice system: the War on Drugs. The use of hip-hop culture, public rhetoric, and mass media as evidence was guided by a “new social movement” theoretical framework that emerged in the early-to-mid 1980s. The goal was to reimagine hip-hop-generated political activism during the height of the War on Drugs through the prism social movement theory to determine hip-hop's function as a Black sociopolitical struggle. The hip-hop social movement consisted of cultural productions in rap, politicized hip-hop films, anti-state critiques in rap journalism, and sociopolitical statements that hip-hop activists made in the mass-media. They produced political critiques that condemned hyper-social surveillance, extraordinary scrutiny, militarized policing, as well as mass incarceration. In doing so, the examined participants effectively placed the government, crime politics, and the criminal justice system on proverbial trial. The main points of this dissertation include the carceral state and how it plagued Black life in the post-civil rights era. Hip-hop-generated activism that nationalized the destruction of a racialized carceral state. Also, hip-hop activists that consisted of rappers such as Public Enemy, KRS-ONE, NAS, Ice-T, N.W.A., and 2Pac Shakur; filmmakers like Spike Lee, John Singleton, and Albert and Allen Hughes; as well as a handful of hip-hop jour (open full item for complete abstract)

Master of Science, The Ohio State University, 2021, Civil Engineering

The COVID-19 pandemic has put a spotlight on the importance for surveillance tools in monitoring disease spread. Wide scale, rapid human testing and environmental wastewater monitoring have been utilized to inform public health decisions relating to COVID-19. While it is known that infected individuals shed virus through actions like coughing and sneezing, viral load on indoor surfaces and their potential to be utilized for surveillance is poorly understood. In this work tested three different sample types from the isolation rooms of students who tested positive for COVID-19. Sample types included surface swabs throughout the room, bulk dust samples, and samples from a passive air sampler. Dust samples were collected from individual homes as well as from congregate living facilities. Viral RNA was extracted from each of the sample types and quantified on RT-qPCR and two types of digital PCR. Bulk dust samples from congregate living facilities were extracted over a period of four weeks to test for viral decay. This study did not measure infectivity of SARS-CoV-2 in collected samples. SARS-CoV-2 was detected in 88% of all dust samples averaged across the three PCR with viral concentration ranging from non-detects to 23,049 copies/mg-dust. By comparison, an average of 55% of surface swabs and 49% of passive air samples tested positive for SARS-CoV-2. 97% of dust samples tested positive for SARS-CoV-2 using droplet digital PCR, which had the lowest detection limit. Interestingly, SARS-CoV-2 viral RNA did not measurably decay over four weeks (R2= 0.009, p=0.47). These findings indicate that dust is an effective matrix for SARS-CoV-2 detection in the indoor environment, even with the application of disinfectants. Persistence of viral RNA in dust will be important in monitoring in order to consider whether high viral loads may be from current or past infections. While large scale rapid human testing is the most accurate tool for monitoring spread, this method is (open full item for complete abstract)

Master of Mathematical Sciences, The Ohio State University, 2021, Mathematics

Universities across the globe have adopted numerous intervention strategies in an attempt to curtail the spread of COVID-19 and keep students, faculty, and staff safe. A common strategy employed in on-campus student populations is that of frequent surveillance testing leading to isolation, contact tracing, and quarantine of suspected exposures. Limiting the spread of infection within the on-campus student body without placing undue burden on quarantine and isolation resources is a key question for university leadership. By examining the interplay between test frequency and test sensitivity under varying forces of infection it is shown that, in the absence of vaccination, frequent testing must be employed to limit the spread of infection while also minimizing the strain on quarantine and isolation resources. Testing twice-weekly using a test of high or low sensitivity for R0 values in the range of 2 to 3 generally contains the spread of COVID-19 without placing undue burden on quarantine and isolation resources. It is important to note that testing regimes that yield low quarantine and isolation burden do not necessarily lead to sucient mitigation of infection in a large university population.