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HMoob Eldership as Pedagogy: Reclaiming HMoob Knowledge as HMoob Education
Get PDFFor centuries non-HMoob people and scholars have sought to label and define who HMoob people are, but this paper reclaims and repositions HMoob people as agentic and reinforces our people’s power to define themselves as we have for centuries. This paper also addresses problematic discourse about HMoob people and HMoob education, such as “peb HMoob tsis muaj kev kawm”1 or “peb tsis muaj kev txawj hab kev ntse le lwm paab lwm pawg.”2 Departing from such deficit discourse, this paper explores HMoob eldership as pedagogy as one way that HMoob people have valid knowledge systems. HMoob eldership as pedagogy examines who is considered an educator and how education is implemented within our community. I propose HMoob eldership as pedagogy as a form of knowledge from our community’s multiple pedagogies and education methodologies, one that roots HMoob epistemologies in eldership. Eldership, in this sense, traces the ways HMoob knowledge is passed down from the older generations to the younger generations through everyday teaching and learning. Ultimately, this work aims to nuance HMoob people’s understanding of HMoob epistemologies and cosmologies.
Thaum yug tseem yau, yug saib moog rua qhov twg los muaj cov laug nyob rua qhov hov lug coj peb, qhuab qha peb, hab hlub peb. Ua caag lug moog txug rua nub nua, yug saib moog rua txuj kaab txuj ke twg los tsis pum ib tug laug nyob rua qhov twg le lawm? Lub sib hawm thaud moog rua qhov twg lawm? Cov laug tsawg zuj zug, leej twg yuav lug sawv puab qhov chaw? Zag nuav, yuav yog koj hab yuav yog kuv. Peb yuav tau dlhau moog ua cov laug lawm. Thaum ti teg ti taw, tsis muaj tug lug qhuab lug qha, peb yuav ncu txug tej laug. Yog vim le nuav, koj hab kuv, peb suav dlawg yuav tsum kawm hab paub txug tej laug tej kev qhuab kev qha. Peb yuav tau xyum hab ncu txug peb HMoob kev qhuab kev qha.3 (Collective Teaching, n.d.
Digital Halftoning and Gamut Mapping for an Inkjet Nail Printer and Digital Halftoning and Descreening with Deep Learning
No full textIn this dissertation, we propose four novel digital image processing algorithms. First, we discuss a novel digital halftoning algorithm that efficiently removes halftone artifacts commonly associated with error diffusion while adding only an insignificant computational cost. Second, we propose a novel gamut mapping algorithm that utilizes the entire printer gamut resulting in more saturated print results. Third, we propose two digital halftoning algorithms using deep neural networks that generate halftones with quality comparable to those generated with the direct binary search (DBS) algorithm. Lastly, we propose a descreening algorithm based on generative adversarial networks (GAN) framework that generates images with realistic texture.Error diffusion algorithms are commonly used for digital halftoning, but they are known to suffer from halftone artifacts. The halftoning algorithm proposed in this dissertation alleviates this issue by adding two features. First, we add a dot-off-dot feature for better halftone texture. Second, we propose a blending-in of the DBS-screened halftone image to the original image as a preprocessing step. These result in halftone images with better texture and reduced artifacts. We demonstrate this by providing a comparison of digitally simulated halftone images.Next, we discuss the gamut mapping algorithm for an inkjet printer, which maps all the colors within the input image to colors reproducible with the printer. Formerly used algorithms for this step suffer from visible desaturation since they only exploit the part of the printer gamut that overlaps with the sRGB gamut. To solve this issue, we add a step we call gamut alignment, which enables the printer to fully exploit the entire printer gamut. We show both the digitally simulated gamut mapped images and the print results from the printer to illustrate the benefits of the added step.We also investigate applying two deep generative models to digital halftoning with the aim of generating halftones with comparable quality to those generated with the DBS algorithm. For the first framework, we apply conditional GANs using two discriminators with different receptive field size and a generator consisting of densely connected blocks. For the second framework, deep autoregressive (AR) models, we propose mapping input images into a feature space using a single forward pass of a deep neural network and then applying a shallow autoregressive model at the end output. Our methods show promising results; halftones generated with our algorithms are less noisy than those generated with a DBS screen and do not contain artifacts commonly associated with error diffusion type algorithms.Lastly, we propose a GAN-based descreening algorithm that generates reconstructed images with realistic texture. Current state-of-the-art descreening algorithms have two issues: first, they mostly are PSNR-oriented reconstruction algorithms, which tend to generate blurry images that lack texture. Furthermore, these algorithms are typically trained with halftone images generated using the Floyd-Steinberg error diffusion algorithm, which is known to generate visible artifacts. To address these issues, first, we propose a GAN-based descreening algorithm that generate images with abundant texture. Next, we propose using the DBS algorithm instead of Floyd-Steinberg error diffusion for generating the halftone images for the training dataset. Both qualitative and quantitative comparisons show that our algorithm outperforms state-of-the-art descreening algorithms significantly
MPC for the Real World: Improving Performance and Usability Via Program Analysis, and Applications to Decentralized Finance
No full textMulti-Party Computation (MPC) is a cryptographic technique that enables a number of parties to perform useful computations while keeping their inputs private. The challenge is that, despite many significant advances over the last decade, (1) it remains orders of magnitude slower than its insecure counterpart, and (2) it is largely inaccessible to non-experts; writing efficient applications requires a deep understanding of strengths and weaknesses of various MPC primitives. The primary focus of this work is to make contributions toward overcoming these challenges. To this end, we develop black-box optimization techniques that require no input from the application programmer.Our first contribution is optimal protocol mixing. Various MPC protocols are inherently well-suited for some tasks and not for others. Thus mixing protocols according to their strengths achieves better efficiency. Unfortunately, a straightforward greedy approach does not work because various protocols have incompatible states. One must appropriately convert the state from one protocol to another when stitching protocols together. We call this problem the Optimal Protocol Assignment (OPA) problem and formulate it as an Integer Program (IP). We prove that, contrary to the long-held conjecture that OPA is NP-Hard, the problem can be solved in polynomial time for the case of 2 protocols. We have implemented our technique. Our evaluation shows that, whereas previous heuristics-based techniques could take from several minutes to several hours, our optimal solution takes seconds to mix a variety of benchmarks.Our second contribution is better MPC scheduling. We first prove that, like its classical counterpart, optimal scheduling remains NP-Hard despite the simpler structure of MPC programs. We propose a compiler framework (front-end, middle-end, and back-end), and for the middle-end, we provide a formal description of an intermediate representation (IR) that we call MPC-IR. Next, we devise a backend-independent technique for automatic vectorization. Our vectorization algorithm takes un-optimized MPC-IR, replaces scalar operations with vectorized counterparts, and outputs optimized MPC-IR. We prove the correctness of this transformation and our extensive evaluation—on a set of 15 benchmarks, the largest in this area—shows that vectorization reduces circuit evaluation time by upto 55x (BMR, 3-party setting) and communication per channel by up to 13x (GMW setting).Finally, recognizing that blockchains provide a unique opportunity to solve “trust” problem through rewards and punishments, we present a design for fast blockchain-based applications. The specific application we devise is a frontrunning resilient market maker (MM). In contrast to the previous works that either require expensive cryptographic tools or additional trust assumptions, we only use lightweight cryptography and game theory. We prove the correctness and security of our construction. Our evaluation shows our performance to be an order of magnitude better than MPC-based constructions. Compared to Uniswap, the largest Decentralized Exchange (DEX), we are 2x better in performance. Moreover, our transactions—being essentially payment transactions—have constant gas costs compared to the variable gas costs of DEXes. This is despite that no current DEX provides defense against frontrunning
Ranking Social Engineering Attack Vectors in the Healthcare and Public Health Sector
No full textThe National Institute of Standards and Technology defines social engineering as an attack vector that deceives an individual into divulging confidential information or performing unwanted actions [1]. Different methods of social engineering include phishing, pretexting, tailgating, baiting, vishing, SMSishing, and quid pro quo. These attacks can have devastating effects, especially in the healthcare sector, where there are budgetary and time constraints. To address these issues, this study aimed to use cybersecurity experts to identify the most important social engineering attacks to the healthcare sector and rank the underlying factors in terms of cost, success rate, and data breach. By creating a ranking that can be updated constantly, organizations can provide more effective training to users and reduce the overall risk of a successful attack. This study identified phishing attacks via email, voice and SMS to be the most important to defend against primarily due to the number of attacks. Baiting and quid pro quo consistently ranked as lower in priority and ranking
Evaluator Feedback: Do Teachers Get what they Want or Need?
No full textThis qualitative study aimed to identify teaching competencies teachers attribute to student achievement and what evaluator feedback they want and need to improve their instruction. Participants from one school district were interviewed. The data were coded through the lens of Marzano’s (2017) Art and Science of Teaching Framework. Of Marzano’s 60 competencies, teachers overwhelmingly focused on competencies around engaging students, specifically noticing when students are not engaged. The participants in this study desired evaluator feedback on strategies to better engage students, yet did not identify, by name or insinuation, those teaching competencies critical to actually engaging students, such as planning and preparing content material or how to differentiate the delivery of content to meet each student’s needs. In essence, the art of teaching trumps the science of teaching when it comes to what teachers focus their instruction on and what they want feedback on. The data from this study also suggested participants were more likely to use feedback given by an evaluator if they respected the evaluator and their professional knowledge and skill in the teacher’s content area
Design of Lunar Transfer Trajectories for Secondary Payload Missions
No full textSecondary payloads have a rich and successful history of utilizing cheap rides to orbit to perform outstanding missions in Earth orbit, and more recently, in cislunar space and beyond. New launch vehicles, namely the Space Launch System (SLS), are increasing the science opportunity for rideshare class missions by providing regular service to the lunar vicinity. However, trajectory design in a multi-body regime brings a host of novel challenges, further exacerbated by constraints generated from the primary payload’s mission. Often, secondary payloads do not possess the fuel required to directly insert into lunar orbit and must instead perform a lunar flyby, traverse the Earth-Moon-Sun system, and later return to the lunar vicinity. This investigation develops a novel framework to construct low-cost, end-to-end lunar transfer trajectories for secondary payload missions. The proposed threephase approach provides unique insights into potential lunar transfer geometries. The phases consist of an arc from launch to initial perilune, an exterior transfer arc, and a lunar approach arc. The space of feasible transfers within each phase is determined through low-dimension grid searches and informed filtering techniques, while the problem of recombining the phases through differential corrections is kept tractable by reducing the dimensionality at each phase transition boundary. A sample mission demonstrates the trajectory design approach and example solutions are generated and discussed. Finally, alternate strategies are developed to both augment the analysis and for scenarios where the proposed three-phase technique does not deliver adequate solutions. The trajectory design methods described in this document are applicable to many upcoming secondary payload missions headed to lunar orbit, including spacecraft with only low-thrust, only high-thrust, or a combination of both
A Framework for Enhancing Pedestrian Service at Signalized Intersections
No full textHistorically, roadway performance measures have focused almost exclusively on vehicular movement. In most urban settings, pedestrian movements typically outnumber vehicular movements significantly. However, historically there has been no way to collect such data at scale in a systematic manner. With the widespread introduction of cameras for monitoring vehicular flow, there is an opportunity to leverage this infrastructure to acquire insights into the patterns and trends of pedestrian activities at signalized intersections in an automated and systematic manner. Such data and performance measures are critical inputs for detailed analysis of pedestrian movements. Overall, addressing this issue is a vital component of transportation agencies that seek to develop equitable treatment of all transportation system users including vulnerable road users. This dissertation addresses the gap in the literature regarding detailed characterization of pedestrian movement patterns and trends. The dissertation leverages data from signalized intersection cameras to (1) quantify the required duration for the pedestrian walk-interval based on pedestrian volume and geometric features of the intersection, (2) carry out time series analysis to acquire insights on pedestrian demand patterns and the influential variables, and (3) build machine learning algorithms to accurately predict pedestrian volumes and tie it to signal timing, to enhance service for all roadway users.The first study provides quantitative guidance for walk time interval selection. This part reports on 1,500 pedestrian movement observations from 12 signalized intersections with varying pedestrian demand, pedestrian storage areas, and pedestrian push-button locations. That data were used to develop a model predicting start-up time with an R2 of 0.89. The study concludes by presenting a quantitative table with four timing categories ranging from negligible volume to high volume and corresponding appropriate durations for the pedestrian walk interval time, based on the demand per cycle, storage area for pedestrians, and offset of the pedestrian push-button from the crosswalk.The second study describes several scalable techniques for measuring and analyzing the movement of pedestrians on a typical university campus. Approximately 35.6 million pedestrian movements over 19 months were tabulated in 15-minute counts of pedestrian volumes by intersection. Counts are used in evaluating pedestrian activity dependency on select explanatory variables at both the network and intersection levels at each time step for the entire analysis period.The study reports on time series correlation and cross-correlation and measures the timedependency between pedestrian activities and influential factors such as the academic calendar, football games, basketball games, and graduation ceremonies. It provides a comprehensive understanding of the factors that are most influential of pedestrian volumes at intersections.The third study presents a data-driven approach to predict pedestrian volume per intersection quadrant at 15-minute intervals, and to connect this information to signal timing. Machine learning random forest and XGBoost classification models were trained on a large dataset of pedestrian counts consisting of approximately 2.6 million observations collected through 19 months at 13 exclusive pedestrian service intersections. The predicted pedestrian volumes were then categorized per the pedestrian walk-interval categories to provide optimal signal timing for each intersection quadrant, thus enabling potential dynamic pedestrian signal timing at exclusive service intersections
A Multimethod Approach to Identify Factors and Improve the Process of Deprescribing Anticholinergics in Older Adults
No full textPolypharmacy in older adults presents several challenges, such as suboptimal therapeutic outcomes and increased adverse effects. Deprescribing, a clinically supervised process of decreasing dosage or stopping the medication when risks outweigh benefits, has emerged as one possible solution to these problems. However, the literature describing deprescribing intervention frameworks is heterogenous regarding targeted medications to deprescribe, population characteristics, clinical settings, and measured outcomes. This dissertation utilizes Linsky et al.\u27s deprescribing conceptual model, which details factors influencing decisions regarding initiating deprescribing interventions and their direct impact on the process.This dissertation utilizes a multimethod approach to investigate factors that facilitate and improve the deprescribing of anticholinergic medications for older adults, addressing gaps in this population\u27s anticholinergic medication use. The three studies included in this dissertation provide a comprehensive understanding of deprescribing anticholinergic medications for this population, each contributing unique insights and results.The first study explores the feasibility of in-person and remote Home Medication Inventory Method (HMIM) approaches to evaluate over-the-counter (OTC) and prescription medication possession and use, including anticholinergics. Results demonstrate that both methods can accurately assess anticholinergic medication usage patterns, providing healthcare providers with reproducible methods and detailed medication profiles to make informed deprescribing decisions based on complete medication lists.The second study examined the intertwined roles of social determinants of health and health beliefs in predicting older adults\u27 self-reported deprescribing behaviors, proposing the Deprescribing Health Belief Model (DeRx-HBM) framework that can be utilized for these efforts. These results emphasize the importance of considering these elements when creating a patient-centric and culturally sensitive intervention since they significantly shape deprescribing behaviors.In the third study, we explored the use of a symptom-specific scale for measuring the symptom burden in older adults during the deprescribing of anticholinergic medications prescribed for urinary incontinence, depression, and pain management. This research introduces a validated scale for assessing anticholinergic symptom burden prior to, throughout, and following the deprescribing attempt. The implementation of this scale has the potential to enhance the reproducibility and standardization of deprescribing decisions. Furthermore, it can improve communication between healthcare professionals and patients, as well as monitor the effectiveness of interventions during and after the deprescribing process.Collectively, these studies provide invaluable insights into factors influencing deprescribing decisions, obstacles to implementing deprescribing practices, and potential strategies to optimize medication management in older adults. The major takeaway from these studies is that addressing these factors leads to more informed decisions among healthcare professionals and patients - potentially leading to improved patient outcomes, ensure the ongoing effectiveness of deprescribing initiatives among older adults, and the promotion of health equity throughout the deprescribing process
A Positioning Theory Analysis of Interaction Surrounding Design Failures in an Elementary Engineering Club
Get PDFThis qualitative study applies Positioning Theory to identify positions that mediate the experiences of design failure within the context of an afterschool engineering club (EC) with elementary students diverse in language, race, ethnicity, gender, and academic abilities. We ask: (1) What kinds of structural design failure and failure responses did participants in EC experience? and (2) What are students’ and teachers’ positions in relation to responses to design failure? Types of positions (e.g., builder, tinkerer, idea-elicitor, director, observer) were identified in relation to children’s and teachers’ actions and speech in response to structural design failure during EC. Participants included 12 third-grade students and four teachers involved in EC for eight weeks. Data sources include audio transcripts, video, and field notes. Twenty-four design failure episodes were identified and transcribed multimodally from video, followed by coding of episodes using a multimodal Positioning Theory analytical framework. Findings discuss the kinds of engineering design actions and associated positionings unfolding in response to failure as well as the positions mediating teacher and student responses to design. We highlight the importance of student and teacher mediation as well as how Positioning Theory can be used to expand our understanding of (re)positionings that can occur within responses to design failure. Specifically, elementary engineering curricular materials must create the context to support the range of positions taken up in response to design failure. This includes explicit modeling of discursive actions surrounding design failure, multiple opportunities for students to experience and respond to design failure with time to improve beyond the design–build–test model, and support for teachers to address the range of students’ responses to design failures knowledgeably and flexibly
Student Preferences for Reference Services at a Remote Biological Station Library
Get PDFDuring the 2020 and 2021 summer semesters, the University of Michigan Biological Station (UMBS) transitioned to hybrid classes that were primarily distance learning with two-week inperson sections. The library offered both synchronous and asynchronous reference assistance over the summer term. An analysis showed that students favored using the UMBS LibGuide over synchronous virtual reference help via Zoom. Students further preferred face-to-face interactions over virtual formats, and their preference for LibGuide assistance may carry into the post- COVID-19 classroom. This finding suggests that students prioritize convenience and immediacy over personalized assistance in the Zoom platform. Thus, in providing reference assistance to student populations in the field sciences, balancing face-to-face interactions with convenience and immediacy should be a priority. Recommendations based on the success of the 2020 and 2021 field seasons were suggested for reference interactions in future field courses