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    2023 Celebration of Faculty Scholarship

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    A bibliography of works featured in the 2023 Celebration of Faculty Scholarship event sponsored by Loyola University Chicago Libraries. The event featured articles, books, and other materials created by Loyola faculty members in the academic year 2022-2023

    Revisiting Marcuse on Repressive Tolerance: A Twenty-First Century Retrospective

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    Herbert Marcuse’s essay Repressive Tolerance (RP) has been praised by the Left and vilified by the Right for its alleged promotion of censorship targeting reactionary opinions and actions. I argue that this interpretation of the text is mistaken. According to my alternative reading of the text, RP should be understood as an exercise in provocation and irony aimed at defending civil disobedience and dissent. Marcuse’s defense of dissent, however, appeals to a critique of pure tolerance that exposes the unavoidably partisan practice of tolerance, which invariably favors the reigning undemocratic status quo. The choice whether to tolerate in a manner that discriminates in favor of opinions and actions that are likely to advance the democratic conditions essential to a more just and epistemically sound exercise of toleration or to discriminate in favor of those that hinder them ---a distinction, as Marcuse sees it, that corresponds to the distinction between the Left and the Right---is dictated by reason itself, whose exercise depends on toleration. Because toleration must discriminate in the absence of full reason, its fallible exercise in favor of reason and democracy must proceed cautiously, not by suppressing voices on the Right so much as selectively encouraging those on the Left. I conclude that because this equation of impartial reason with a partisan view of society is unavoidable regardless of one’s political sympathies accusing Marcuse of being elitist and hypocritical is itself hypocritical

    Exploring Perceptions of Algorithmic Bias in Video Interviewing Software: The Importance of AI Hiring Education

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    Many employers across various fields are incorporating artificial intelligence into their hiring processes. Existing research suggests that although artificial intelligence is often perceived as neutral by its advocates, it can inadvertently perpetuate both implicit and explicit biases present in the engineers and designers responsible for its development through the machine learning process. To delve deeper into this issue, we conducted a research study involving 25 recent graduates from 12 different industries. Our aim was to gain insights into the workings of AI video interviewing software and to understand the reactions of recent graduates who have used this technology. Considering the pivotal role of computing and technology education in shaping future careers, we believe that our investigation can assist educators in better preparing their students for a workforce that increasingly relies on artificial intelligence, while also being mindful of its potential biases, particularly in the context of hiring

    Intermediate C Programming (2nd Edition) Supplemental Lecture Materials

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    Intermediate C Programming (2nd Edition) provides a stepping-stone for intermediate-level students to go from writing short programs to writing real programs well. It shows students how to identify and eliminate bugs, write clean code, share code with others, and use standard Linux-based tools, such as ddd and valgrind. This second edition provides expanded coverage of these topics with new material focused on software engineering, including version control and unit testing. The text enhances their programming skills by explaining programming concepts and comparing common mistakes with correct programs. It also discusses how to use debuggers and the strategies for debugging as well as studies the connection between programming and discrete mathematics. This Figshare artifact includes additional student and instructor resources in PowerPoint format. For your convenience, all PowerPoint files can be downloaded via the file all-slides.zip. Permission is granted to use the materials for your own purposes. We only ask that you acknowledge the book (and us) if you decide to use them. Please note that we are still working on additional slides for the new topics but these should become available in 2024

    Worker\u27s Rights

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    This chapter discusses the rights of workers. It focuses on Bangladesh, China, India, and Vietnam

    Terry Riley\u27s In C for Mobile Ensemble

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    This workshop presents a mobile-friendly Web Audio application for a “technology ensemble play-along” of Terry Riley’s 1964 composition In C. Attendees will join in a reading of In C using available web-enabled devices as musical instruments. We hope to demonstrate an accessible music-technology experience that relies on face-to-face interaction within a shared space. In this all-electronic implementation, no special musical or technical expertise is required. Accepted for presentation and publication at WAC 2024

    Robust Source Attribution of Synthetically Generated Western Blot Images

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    Retracted papers commonly include manipulations of images and figures that are unfit for publication. While some manipulations are benign, like increasing contrast or zooming in, others are designed to fool the intended audience. Recent improvements in generative computer vision pose a security risk to scientific review since generated images are often indistinguishable from authentic bioscience evidence; even to field experts. In this work, we improve upon previous attempts to detect synthetic images by attending to their differences in the frequency domain. Additionally, we solve the multi- class classification of synthetic Western blots and attribute Western blot images to their respective generative model architecture. We demonstrate that our method outperforms previous methods for synthetic Western blot detection; including efforts to classify JPEG compressed images. Please note that this is a technical report based on preliminary research results

    An Automated Approach for Improving The Inference Latency and Energy Efficiency of Pretrained CNNs by Removing Irrelevant Pixels with Focused Convolutions

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    Computer vision often uses highly accurate Convolutional Neural Networks (CNNs), but these deep learning models are associated with ever-increasing energy and computation requirements. Producing more energy-efficient CNNs often requires model training which can be cost-prohibitive. We propose a novel, automated method to make a pretrained CNN more energy-efficient without re-training. Given a pretrained CNN, we insert a threshold layer that filters activations from the preceding layers to identify regions of the image that are irrelevant, i.e. can be ignored by the following layers while maintaining accuracy. Our modified focused convolution operation saves inference latency (by up to 25%) and energy costs (by up to 22%) on various popular pretrained CNNs, with little to no loss in accuracy

    PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source Software

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    The development and training of deep learning models have become increasingly costly and complex. Consequently, software engineers are adopting pre-trained models (PTMs) for their downstream applications. The dynamics of the PTM supply chain remain largely unexplored, signaling a clear need for structured datasets that document not only the metadata but also the subsequent applications of these models. Without such data, the MSR community cannot comprehensively understand the impact of PTM adoption and reuse. This paper presents the PeaTMOSS dataset, which comprises metadata for 281,638 PTMs and detailed snapshots for all PTMs with over 50 monthly downloads (14,296 PTMs), along with 28,575 open-source software repositories from GitHub that utilize these models. Additionally, the dataset includes 44,337 mappings from 15,129 downstream GitHub repositories to the 2,530 PTMs they use. To enhance the dataset’s comprehensiveness, we developed prompts for a large language model to automatically extract model metadata, including the model’s training datasets, parameters, and evaluation metrics. Our analysis of this dataset provides the first summary statistics for the PTM supply chain, showing the trend of PTM development and common shortcomings of PTM package documentation. Our example application reveals inconsistencies in software licenses across PTMs and their dependent projects. PeaTMOSS lays the foundation for future research, offering rich opportunities to investigate the PTM supply chain. We outline mining opportunities on PTMs, their downstream usage, and crosscutting questions. Our artifact is available at https://github.com/PurdueDualityLab/PeaTMOSS-Artifact

    Overview of Instruments of Measurement for Perceived Discrimination

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    The purpose of this study is to examine the current tools of measurement for perceived discrimination. Specifically, to investigate the number of tools of measurement available, similarities and differences between different tools available, and possible gaps in these tools of measurement. Literature shows the impact of perceived discrimination on both physical and mental health, but little literature exists on the quality and quantity of tools of measurement for perceived discrimination. By investigating current tools of measurement improvements may be made in the collection and analysis of such data


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