Identifying Conflicting Incentives in United States Federal Cybersecurity Policy: A Sociotechnical Systems Approach

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Description
Despite increased attention and funding from companies and governments worldwide over the past several years, cybersecurity incidents (such as data breaches or exploited vulnerabilities) remain frequent, widespread, and severe. Policymakers in the United States have generally addressed these problems discretely,…

Despite increased attention and funding from companies and governments worldwide over the past several years, cybersecurity incidents (such as data breaches or exploited vulnerabilities) remain frequent, widespread, and severe. Policymakers in the United States have generally addressed these problems discretely, treating them as individual events rather than identifying commonalities between them and forming a more effective broad-scale solution. In other words: the standard approaches to cybersecurity issues at the U.S. federal level do not provide sufficient insight into fundamental system behavior to meaningfully solve these problems. To that end, this dissertation develops a sociotechnical analogy of a classical mechanics technique, a framework named the Socio-Technical Lagrangian (STL). First, existing socio/technical/political cybersecurity systems in the United States are analyzed, and a new taxonomy is created which can be used to identify impacts of cybersecurity events at different scales. This taxonomy was created by analyzing a vetted corpus of key cybersecurity incidents, each of which was noted for its importance by multiple respected sources, with federal-level policy implications in the U.S.. The new taxonomy is leveraged to create STL, an abstraction-level framework. The original Lagrangian process, from the physical sciences, generates a new coordinate system that is customized for a specific complex mechanical system. This method replaces a conventional reference frame –one that is ill-suited for the desired analysis –with one that provides clearer insights into fundamental system behaviors. Similarly, STL replaces conventional cybersecurity analysis with a more salient lens, providing insight into the incentive structures within cybersecurity systems, revealing often hidden conflicts and their effects. The result is not a single solution, but a new framework that allows several questions to be asked and answered more effectively. Synthesizing the findings from the taxonomy and STL framework, the third contribution involves formulating reasonable and effective recommendations for enhancing the cybersecurity system's state for multiple stakeholder groups. Leveraging the contextually appropriate taxonomy and unique STL framework, these suggestions address the reform of U.S. federal cybersecurity policy, drawing insights from various governmental sources, case law, and discussions with policy experts, culminating in analysis and recommendations around the 2023 White House Cybersecurity Strategy.
Date Created
2023
Agent

Combinatorial Inventions in Artificial Intelligence: Empirical Evidence and Implications for Science, Technology, and Organizations

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Description
Artificial Intelligence (AI) is a rapidly advancing field with the potential to impact every aspect of society, including the inventive practices of science and technology. The creation of new ideas, devices, or methods, commonly known as inventions, is typically viewed…

Artificial Intelligence (AI) is a rapidly advancing field with the potential to impact every aspect of society, including the inventive practices of science and technology. The creation of new ideas, devices, or methods, commonly known as inventions, is typically viewed as a process of combining existing knowledge. To understand how AI can transform scientific and technological inventions, it is essential to comprehend how such combinatorial inventions have emerged in the development of AI.This dissertation aims to investigate three aspects of combinatorial inventions in AI using data-driven and network analysis methods. Firstly, how knowledge is combined to generate new scientific publications in AI; secondly, how technical com- ponents are combined to create new AI patents; and thirdly, how organizations cre- ate new AI inventions by integrating knowledge within organizational and industrial boundaries. Using an AI publication dataset of nearly 300,000 AI publications and an AI patent dataset of almost 260,000 AI patents granted by the United States Patent and Trademark Office (USPTO), this study found that scientific research related to AI is predominantly driven by combining existing knowledge in highly conventional ways, which also results in the most impactful publications. Similarly, incremental improvements and refinements that rely on existing knowledge rather than radically new ideas are the primary driver of AI patenting. Nonetheless, AI patents combin- ing new components tend to disrupt citation networks and hence future inventive practices more than those that involve only existing components. To examine AI organizations’ inventive activities, an analytical framework called the Combinatorial Exploitation and Exploration (CEE) framework was developed to measure how much an organization accesses and discovers knowledge while working within organizational and industrial boundaries. With a dataset of nearly 500 AI organizations that have continuously contributed to AI technologies, the research shows that AI organizations favor exploitative over exploratory inventions. However, local exploitation tends to peak within the first five years and remain stable, while exploratory inventions grow gradually over time. Overall, this dissertation offers empirical evidence regarding how inventions in AI have emerged and provides insights into how combinatorial characteristics relate to AI inventions’ quality. Additionally, the study offers tools to assess inventive outcomes and competence.
Date Created
2023
Agent

Electric Skateboards as a Solution to the Last Mile of Personal Transportation

Description

Personal electric vehicles, or PEVs, help individuals navigate short to mid distance commutes in environments that lack effective public transportation solutions. This is known as the “Last Mile” problem. A particular solution, electric skateboards, are highly energy efficient due to…

Personal electric vehicles, or PEVs, help individuals navigate short to mid distance commutes in environments that lack effective public transportation solutions. This is known as the “Last Mile” problem. A particular solution, electric skateboards, are highly energy efficient due to their size but lack auxiliary features for safety and user-convenience connected to the same battery supply. Plus, almost all conventional electric boards come with proprietary software and hardware designs, meaning that modifying or improving upon their logic is extremely difficult if not impossible. Therefore, our group aims to prototype an improved, open-source electric skateboard design to determine the feasibility of our ideas.

Date Created
2023-05
Agent

Electric Skateboards as a Solution to the Last Mile of Personal Transportation

Description

Personal electric vehicles, or PEVs, help individuals navigate short to mid distance commutes in environments that lack effective public transportation solutions. This is known as the “Last Mile” problem. A particular solution, electric skateboards, are highly energy efficient due to…

Personal electric vehicles, or PEVs, help individuals navigate short to mid distance commutes in environments that lack effective public transportation solutions. This is known as the “Last Mile” problem. A particular solution, electric skateboards, are highly energy efficient due to their size but lack auxiliary features for safety and user-convenience connected to the same battery supply. Plus, almost all conventional electric boards come with proprietary software and hardware designs, meaning that modifying or improving upon their logic is extremely difficult if not impossible. Therefore, our group aims to prototype an improved, open-source electric skateboard design to determine the feasibility of our ideas.

Date Created
2023-05
Agent

"Can I Consider You My Friend?" Moving Beyond One-Sided Conversation in Social Robotics

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Description
As people begin to live longer and the population shifts to having more olderadults on Earth than young children, radical solutions will be needed to ease the burden on society. It will be essential to develop technology that can age with…

As people begin to live longer and the population shifts to having more olderadults on Earth than young children, radical solutions will be needed to ease the burden on society. It will be essential to develop technology that can age with the individual. One solution is to keep older adults in their homes longer through smart home and smart living technology, allowing them to age in place. People have many choices when choosing where to age in place, including their own homes, assisted living facilities, nursing homes, or family members. No matter where people choose to age, they may face isolation and financial hardships. It is crucial to keep finances in mind when developing Smart Home technology. Smart home technologies seek to allow individuals to stay inside their homes for as long as possible, yet little work looks at how we can use technology in different life stages. Robots are poised to impact society and ease burns at home and in the workforce. Special attention has been given to social robots to ease isolation. As social robots become accepted into society, researchers need to understand how these robots should mimic natural conversation. My work attempts to answer this question within social robotics by investigating how to make conversational robots natural and reciprocal. I investigated this through a 2x2 Wizard of Oz between-subjects user study. The study lasted four months, testing four different levels of interactivity with the robot. None of the levels were significantly different from the others, an unexpected result. I then investigated the robot’s personality, the participant’s trust, and the participant’s acceptance of the robot and how that influenced the study.
Date Created
2022
Agent

Monocular Visual Odometry: Deep Learning vs Classical Approaches

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Description
Visual Odometry is one of the key aspects of robotic localization and mapping. Visual Odometry consists of many geometric-based approaches that convert visual data (images) into pose estimates of where the robot is in space. The classical geometric methods have…

Visual Odometry is one of the key aspects of robotic localization and mapping. Visual Odometry consists of many geometric-based approaches that convert visual data (images) into pose estimates of where the robot is in space. The classical geometric methods have shown promising results; they are carefully crafted and built explicitly for these tasks. However, such geometric methods require extreme fine-tuning and extensive prior knowledge to set up these systems for different scenarios. Classical Geometric approaches also require significant post-processing and optimization to minimize the error between the estimated pose and the global truth. In this body of work, the deep learning model was formed by combining SuperPoint and SuperGlue. The resulting model does not require any prior fine-tuning. It has been trained to enable both outdoor and indoor settings. The proposed deep learning model is applied to the Karlsruhe Institute of Technology and Toyota Technological Institute dataset along with other classical geometric visual odometry models. The proposed deep learning model has not been trained on the Karlsruhe Institute of Technology and Toyota Technological Institute dataset. It is only during experimentation that the deep learning model is first introduced to the Karlsruhe Institute of Technology and Toyota Technological Institute dataset. Using the monocular grayscale images from the visual odometer files of the Karlsruhe Institute of Technology and Toyota Technological Institute dataset, through the experiment to test the viability of the models for different sequences. The experiment has been performed on eight different sequences and has obtained the Absolute Trajectory Error and the time taken for each sequence to finish the computation. From the obtained results, there are inferences drawn from the classical and deep learning approaches.
Date Created
2022
Agent

A Proactive Systematic Approach to Enhance and Preserve Users’ Tech Applications Data Privacy Awareness and Control in Smart Cities

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Description
The reality of smart cities is here and now. The issues of data privacy in tech applications are apparent in smart cities. Privacy as an issue raised by many and addressed by few remains critical for smart cities’ success. It…

The reality of smart cities is here and now. The issues of data privacy in tech applications are apparent in smart cities. Privacy as an issue raised by many and addressed by few remains critical for smart cities’ success. It is the common responsibility of smart cities, tech application makers, and users to embark on the journey to solutions. Privacy is an individual problem that smart cities need to provide a collective solution for. The research focuses on understanding users’ data privacy preferences, what information they consider private, and what they need to protect. The research identifies the data security loopholes, data privacy roadblocks, and common opportunities for change to implement a proactive privacy-driven tech solution necessary to address and resolve tech-induced data privacy concerns among citizens. This dissertation aims at addressing the issue of data privacy in tech applications based on known methodologies to address the concerns they allow. Through this research, a data privacy survey on tech applications was conducted, and the results reveal users’ desires to become a part of the solution by becoming aware and taking control of their data privacy while using tech applications. So, this dissertation gives an overview of the data privacy issues in tech, discusses available data privacy basis, elaborates on the different steps needed to create a robust remedy to data privacy concerns in enabling users’ awareness and control, and proposes two privacy applications one as a data privacy awareness solution and the other as a representation of the privacy control framework to address data privacy concerns in smart cities.
Date Created
2022
Agent

Modeling Power: Data Models and the Production of Social Inequality

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Description
Software systems can exacerbate and cause contemporary social inequities. As such, scholars and activists have scrutinized sociotechnical systems like those used in facial recognition technology or predictive policing using the frameworks of algorithmic bias and dataset bias. However,…

Software systems can exacerbate and cause contemporary social inequities. As such, scholars and activists have scrutinized sociotechnical systems like those used in facial recognition technology or predictive policing using the frameworks of algorithmic bias and dataset bias. However, these conversations are incomplete without study of data models: the structural, epistemological, and technical frameworks that shape data. In Modeling Power: Data Models and the Production of Social Inequality, I elucidate the connections between relational data modeling techniques and manifestations of systems of power in the United States, specifically white supremacy and cisgender normativity. This project has three distinct parts. First, I historicize early publications by E. F. Codd, Peter Chen, Miles Smith & Diane Smith, and J. R. Abrial to demonstrate that now-taken-for-granted data modeling techniques were products of their social and technical moments and, as such, reinforced dominant systems of power. I further connect database reification techniques to contemporary racial analyses of reification via the work of Cheryl Harris. Second, I reverse engineer Android applications (with Jadx and apktool) to uncover the relational data models within. I analyze DAO annotations, create entity-relationship diagrams, and then examine those resultant models, again linking them back to systems of race and gender power. I craft a method for performing a reverse engineering investigation within a specific sociotechnical context -- a situated analysis of the contextual epistemological frames embedded within relational paradigms. Finally, I develop a relational data model that integrates insights from the project’s reverse and historical engineering phases. In my speculative engineering process, I suggest that the temporality of modern digital computing is incommensurate with the temporality of modern transgender lives. Following this, I speculate and build a trans-inclusive data model that demonstrates uses of reification to actively subvert systems of racialized and gendered power. By promoting aspects of social identity to first-order objects within a data model, I show that additional “intellectual manageability” is possible through reification. Through each part, I argue that contemporary approaches to the social impacts of software systems incomplete without data models. Data models structure algorithmic opportunities. As algorithms continue to reinforce systems of inequality, data models provide opportunities for intervention and subversion.
Date Created
2022
Agent

Advancing Methods to Monitor and Assess Personal Ultraviolet Radiation Exposure

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Description
Ultraviolet (UV) radiation is the most well-known cause of skin cancer, and skin cancer is the most common type of cancer in the United States. People are exposed to UV rays when they engage in outdoor activities, particularly exercise, which…

Ultraviolet (UV) radiation is the most well-known cause of skin cancer, and skin cancer is the most common type of cancer in the United States. People are exposed to UV rays when they engage in outdoor activities, particularly exercise, which is an important health behavior. Thus, researchers and the general public have shown increasing interest in measuring UV exposures during outdoor physical activity using wearable sensors. However, minimal research exists at the intersection of UV sensors, personal exposure, adaptive behavior due to exposures, and risk of skin damage. Three studies are presented in this dissertation: (1) a state-of-the-art review that synthesizes the current academic and grey literature surrounding personal UV sensing technologies; (2) the first study to investigate the effects of specific physical activity types, skin type, and solar angle on personal exposure in different outdoor environmental contexts; and (3) a study that develops recommendations for future UV-sensing wearables based on follow-up interviews with participants from the second study, who used a wrist-worn UV sensor while exercising outdoors. The first study provides recommendations for 13 commercially available sensors that are most suitable for various types of research or personal use. The review findings will help guide researchers in future studies assessing UV exposure with wearables during physical activity. The second study outlines the development of predictive models for individual-level UV exposure, which are also provided. These models recommend the inclusion of sky view factor, solar angle, activity type, urban environment type, and the directions traveled during physical activity. Finally, based on user feedback, the third study recommends that future UV-sensing wearables should be multi-functional watches where users can toggle between showing their UV exposure results in cumulative and countdown formats, which is intuitive and aesthetically pleasing to users.
Date Created
2021
Agent

Constructing a Visualization Dashboard to Improve Educational Standards in Arizona Legislative Districts

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Description

Education has been at the forefront of many issues in Arizona over the past several years with concerns over lack of funding sparking the Red for Ed movement. However, despite the push for educational change, there remain many barriers to…

Education has been at the forefront of many issues in Arizona over the past several years with concerns over lack of funding sparking the Red for Ed movement. However, despite the push for educational change, there remain many barriers to education including a lack of visibility for how Arizona schools are performing at a legislative district level. While there are sources of information released at a school district level, many of these are limited and can become obscure to legislators when such school districts lie on the boundary between 2 different legislative districts. Moreover, much of this information is in the form of raw spreadsheets and is often fragmented between government websites and educational organizations. As such, a visualization dashboard that clearly identifies schools and their relative performance within each legislative district would be an extremely valuable tool to legislative bodies and the Arizona public. Although this dashboard and research are rough drafts of a larger concept, they would ideally increase transparency regarding public information about these districts and allow legislators to utilize the dashboard as a tool for greater understanding and more effective policymaking.

Date Created
2021-05
Agent
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