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Racializing International Student Discourse in the United States: Recommendations for Counseling Psychology and Narratives from Asian Indian International Students
No full textInternational students are integral in U.S. higher education institutions, and research demonstrates that these students face a range of concerns, with race and racism being understudied. In this dissertation, I present two chapters highlighting the racialized experiences of international students of color. In the first theoretical chapter, I connected international student literature to tenets of Critical Race Theory. I ended with specific recommendations for the field of counseling psychology. In the second empirical chapter, I conducted a narrative inquiry and interviewed 6 Asian Indian international students about how they formed understandings of race and racism in the United States. Through using reflexive thematic analysis, four themes of antiessentialism and intersectionality, social construction of race, sources of racial construction, and impacts of racial construction were developed. Using these results, I provided insights into how international students from India may understand race and implications for clinical practice, higher education, and research
Mechanism Design in Defense Against Offline Password Attacks
No full textThe prevalence of offline password attacks, resulting from attackers breaching authentication servers and stealing cryptographic password hashes, poses a significant threat. Users’ tendency to select weak passwords and reuse passwords across multiple accounts, coupled with computation advancement, further exacerbate the danger.This dissertation addresses this issue by proposing password authentication mechanisms that aim to minimize the number of compromised passwords in the event of offline attacks, while ensuring that the server’s workload remains manageable. Specifically, we present three mechanisms: (1) DAHash: This mechanism adjusts password hashing costs based on the strength of the underlying password. Through appropriate tuning of hashing cost parameters, the DAHash mechanism effectively reduces the fraction of passwords that can be cracked by an offline password cracker. (2) Password Strength Signaling: We explore the application of Bayesian Persuasion to password authentication. The key idea is to have the authentication server store a noisy signal about the strength of each user password for an offline attacker to find. We demonstrate that by appropriately tuning the noise distribution for the signal, a rational attacker will crack fewer passwords. (3) Cost-Asymmetric Memory Hard Password Hashing: We extend the concept of password peppering to modern Memory Hard password hashing algorithms. We identify limitations in naive extensions and introduce the concept of cost-even breakpoints as a solution. This approach allows us to overcome these limitations and achieve cost-asymmetry, wherein the expected cost of validating a correct password is significantly smaller than the cost of rejecting an incorrect password.When analyzing the behavior of a rational attacker it is important to understand the attacker’s guessing curve i.e., the percentage of passwords that the attacker could crack within a guessing budget B. Dell’Amico and Filippone [1] introduced a Monte Carlo algorithm to estimate the guessing number of a password as well as an estimate for the guessing curve. While the estimated guessing number is accurate in expectation the variance can be large and the method does not guarantee that the estimates are accurate with high probability. Thus, we introduce Confident Monte Carlo as a tool to provide confidence intervals for guessing number estimates and upper/lower bound the attacker’s guessing curves.Moreover, we extend our focus beyond classical attackers to include quantum attackers. We present a decision-theoretic framework that models the rational behavior of attackers equipped with quantum computers. The objective is to quantify the capabilities of a rational quantum attacker and the potential damage they could inflict, assuming optimal decision-making. Our framework can potentially contribute to the development of effective countermeasures against a wide range of quantum pre-image attacks in the future
Quantum Activation Functions for Neural Network Regularization
No full textThe Bias-Variance Trade-off, where restricting the size of a hypothesis class can limit the generalization error of a model, is a canonical problem in Machine Learning, and a particular issue for high-variance models like Neural Networks that do not have enough parameters to enter the interpolating regime. Regularization techniques add bias to a model to lower testing error at the cost of increasing training error. This paper applies quantum circuits as activation functions in order to regularize a Feed-Forward Neural Network. The network using Quantum Activation Functions is compared against a network of the same dimensions except using Rectified Linear Unit (ReLU) activation functions, which can fit any arbitrary function. The Quantum Activation Function network is then shown to have comparable training performance to ReLU networks, both with and without regularization, for the tasks of binary classification, polynomial regression, and regression on a multicollinear dataset, which is a dataset whose design matrix is rank-deficient.The Quantum Activation Function network is shown to achieve regularization comparable to networks with L2-Regularization, the most commonly used method for neural network regularization today, with regularization parameters in the range of λ ∈ [.1, .5], while still allowing the model to maintain enough variance to achieve low training error. While there are limitations to the current physical implementation of quantum computers, there is potential for future architecture, or hardware-based, regularization methods that leverage the aspects of quantum circuits that provide lower generalization error
A Spatiotemporal Synthesis of High-Resolution Salinity Data with Aquaculture Applications
Get PDFTechnological advancement and the desire to better monitor shallow habitats in the Chesapeake Bay, Maryland, United States led to the initiation of several high-resolution monitoring programs such as ConMon (short for “Continuous Monitoring”) measuring oxygen, salinity, and chlorophyll-a at a 15-minute frequency. These monitoring efforts have yielded an enormous volume of data and insight into the condition of the tidal water of the Bay. But this information is underutilized in documenting the fine-scale variability of water quality, which is critical in identifying the link between water quality and ecological responses, partly due to the challenges in integrating monitoring data collected at different frequencies and locations. In a project to understand the environmental suitability of aquaculture sites and the future potential overlap between aquaculture and submerged aquatic vegetation, we developed a spatiotemporal synthesis of ConMon data with data from long-term, fixed-station seasonal monitoring. Here, we present our generalized additive model-based approach to predict salinity at high frequency (15 minutes) and fine spatial resolution (~100 meters) in the Maryland portion of the Bay, its major tributaries, and the shallow tidal creeks that exchange with the tributaries. Predictive performance was validated to be 1 PSU (practical salinity unit) in root mean square error using de novo monitoring. The resulting data provide insights into the environmental suitability of aquaculture, specifically the sensitivity of the Easter oyster (Crassostrea virginica) to low salinity stress. The spatiotemporal synthesis approach has potential applications for integrated monitoring and potential linkage with high-resolution water quality models for shallow habitats
An Agent-Based Modeling Approach to Spatial Accessibility
Get PDFPlace-based spatial accessibility represents the ability of populations within geographic units to access goods and services, and thus is an important indicator for sustainable development. Existing spatial accessibility models treat population as simply demand, calculating statistics or optimizing average cost for the population within each geographic unit, rather than modeling individual decisions. This paper proposes AgentAccess, a general-purpose Agent-Based Model (ABM) for spatial accessibility analysis. An ABM framework brings us closer to reality by simulating individual and imperfect decision-making. We introduce the model and compare its results against existing spatial accessibility models using a case study of hospital beds in Cook County, IL, USA
Deep Q-Learning Framework for Quantitative Climate Change Adaptation Policy for Florida Road Network due to Extreme Precipitation
Get PDFClimate change-induced extreme weather and increasing population are increasing the pressure on the global aging road networks. Adaptation requires designing interventions and alterations to the road networks that consider future dynamics of flooding and increased traffic due to the growing population. This paper introduces a reinforcement learning approach to designing interventions for Florida\u27s road network under future traffic and climate projections. Three climate models and a tide and surge model are used to create flooding and coastal inundation projections, respectively. The optimal sequence of decisions for adapting Florida\u27s road network to minimize flooding-related disruptions is solved by using a graph-based deep neural network that assesses the impact of a set of modifications to the road network for road network connectivity and load balance. Results indicate that coastal towns and inland towns where many highways meet require substantial modifications to their road networks to ensure road network connectivity throughout the state in the next 50 years
2023 Seeded Watermelon Cultivar Evaluation in Indiana
Get PDFWatermelons grown in Indiana are primarily red flesh seedless, and a small portion of personal-sized red flesh seedless. Seeded watermelons are typically not grown in large acreages, but they may be used as pollenizer plants for growing seedless watermelons. The 2023 seeded watermelon cultivar trial included 15 seeded watermelon cultivars
Servicescape Effects on Hotel Guests’ Willingness to Pay Premiums at Different Stages of Pandemic: A Multi-Phase Study
Get PDFDrawing on servicescape theory, this research investigates guests’ perceptions of and responses to the protection and prevention practices launched by hotels at different stages of the pandemic. The research finds that hotel guests’ general response-efficacy beliefs positively influence their perception of the effectiveness of the protection and prevention practices adopted in hotels’ physical and social servicescapes, and such positive relationships also show a significant increase from 2020 to 2021. The servicescape effects’ downstream results show that hotel guests are willing to pay premium prices for safety servicescapes manifested as protection and prevention practices implemented at the private space or related to employees. This research sheds light on servicescape theory by deconstructing the overall hotel servicescape concept into multiple dimensions, particularly in a health threat situation such as the pandemic, and empirically examining each dimension’s effects on guests’ monetary response at different timepoints. From a practical perspective, this study provides managerial insights into which servicescape dimensions warrant operational investments by hotels
IMPACT semester report fall 2023: A report by the IMPACT evaluation team
Get PDFThis is the 2023 fall semester report of Instruction Matters: Purdue Academic Course Transformation (IMPACT). IMPACT was created in 2010, and is a large collaborative initiative on the Purdue West Lafayette campus involving multiple key partners across campus including the Office of the Provost, Center for Instructional Excellence (CIE), Purdue Online (PO), Purdue Libraries and School of Information Studies (Libraries), the Evaluation and Learning Research Center (ELRC), and Institutional Data Analytics and Assessment (IDA+A). IMPACT works with instructors to redesign large enrollment, foundational courses with the aim of engaging students more fully in their learning and creating a more student-centered environment, with the expectation that this will improve student success
Mediatization of the Early Automobile: A Visual Analysis of the Illustrated Press in the late 19th and Early 20th century
Get PDFThe paper presents a digital analysis of automobile imagery in the early 20th-century press, examining the mediatization of the anti-car movement and the role images played in conveying and furthering the activist discourse. To investigate the phenomenon, the author compiled and analyzed over 5,000 images from in 185 journals published in 45 cities between 1891 and 1950. The analysis revealed a preponderance of positive representations of the automobile in the press, whilst evidence of negative sentiment towards the automobile, such as protests and accidents, was conspicuously absent, with the exception of satirical publications