311 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Undergraduate Catalog of Studies, 2023-2024

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    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    UMSL Bulletin 2022-2023

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    The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp

    Undergraduate Catalog of Studies, 2022-2023

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    Investigating Trade-offs For Fair Machine Learning Systems

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    Fairness in software systems aims to provide algorithms that operate in a nondiscriminatory manner, with respect to protected attributes such as gender, race, or age. Ensuring fairness is a crucial non-functional property of data-driven Machine Learning systems. Several approaches (i.e., bias mitigation methods) have been proposed in the literature to reduce bias of Machine Learning systems. However, this often comes hand in hand with performance deterioration. Therefore, this thesis addresses trade-offs that practitioners face when debiasing Machine Learning systems. At first, we perform a literature review to investigate the current state of the art for debiasing Machine Learning systems. This includes an overview of existing debiasing techniques and how they are evaluated (e.g., how is bias measured). As a second contribution, we propose a benchmarking approach that allows for an evaluation and comparison of bias mitigation methods and their trade-offs (i.e., how much performance is sacrificed for improving fairness). Afterwards, we propose a debiasing method ourselves, which modifies already trained Machine Learning models, with the goal to improve both, their fairness and accuracy. Moreover, this thesis addresses the challenge of how to deal with fairness with regards to age. This question is answered with an empirical evaluation on real-world datasets

    Towards a New Ontology of Polling Inaccuracy: The Benefits of Conceiving of Elections as Heterogenous Phenomena for the Study of Pre-election Polling Error

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    A puzzle exists at the heart of pre-election polling. Despite continual methodological improvement and repeated attempts to identify and correct issues laid bare by misprediction, average polling accuracy has not notably improved since the conclusion of the Second World War. In this thesis, I contend that this is the result of a poll-level focus within the study of polling error that is both incommensurate with its evolution over time and the nature of the elections that polls seek to predict. I hold that differences between elections stand as a plausible source of polling error and situate them within a novel four-level model of sources of polling error. By establishing the heterogenous nature of elections as phenomena and its expected impact on polling error, I propose a new election-level ontology through which the inaccuracy of polls can be understood. I test the empirical validity of this new ontology by using a novel multi-level model to analyse error across the most expansive polling dataset assembled to date, encompassing 11,832 in-campaign polls conducted in 497 elections across 83 countries, finding that membership within different elections meaningfully impacts polling error variation. With the empirical validity of my proposed ontology established, I engage in an exploratory analysis of its benefits, finding electoral characteristics to be useful in the prediction of polling error. Ultimately, I conclude that the adoption of a new, multi-level ontology of polling error centred on the importance of electoral heterogeneity not only offers a more comprehensive theoretical account of its sources than current understandings, but is also more specifically tailored to the reality of pre-election polling than existing alternatives. I also contend that it offers pronounced practical benefits, illuminating those circumstances in which polling error is likely to vary

    Supervised Preference Models: Data and Storage, Methods, and Tools for Application

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    In this thesis, we present a variety of models commonly known as pairwise comparisons, discrete choice and learning to rank under one paradigm that we call preference models. We discuss these approaches together with the intention to show that these belong to the same family and show a unified notation to express these. We focus on supervised machine learning approaches to predict preferences, present existing approaches and identify gaps in the literature. We discuss reduction and aggregation, a key technique used in this field and identify that there are no existing guidelines for how to create probabilistic aggregations, which is a topic we begin exploring. We also identify that there are no machine learning interfaces in Python that can account well for hosting a variety of types of preference models and giving a seamless user experience when it comes to using commonly recurring concepts in preference models, specifically, reduction, aggregation and compositions of sequential decision making. Therefore, we present our idea of what such software should look like in Python and show the current state of the development of this package which we call skpref

    Formal Methods for Trustworthy Voting Systems : From Trusted Components to Reliable Software

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    Voting is prominently an important part of democratic societies, and its outcome may have a dramatic and broad impact on societal progress. Therefore, it is paramount that such a society has extensive trust in the electoral process, such that the system’s functioning is reliable and stable with respect to the expectations within society. Yet, with or without the use of modern technology, voting is full of algorithmic and security challenges, and the failure to address these challenges in a controlled manner may produce fundamental flaws in the voting system and potentially undermine critical societal aspects. In this thesis, we argue for a development process of voting systems that is rooted in and assisted by formal methods that produce transparently checkable evidence for the guarantees that the final system should provide so that it can be deemed trustworthy. The goal of this thesis is to advance the state of the art in formal methods that allow to systematically develop trustworthy voting systems that can be provenly verified. In the literature, voting systems are modeled in the following four comparatively separable and distinguishable layers: (1) the physical layer, (2) the computational layer, (3) the election layer, and (4) the human layer. Current research usually either mostly stays within one of those layers or lacks machine-checkable evidence, and consequently, trusted and understandable criteria often lack formally proven and checkable guarantees on software-level and vice versa. The contributions in this work are formal methods that fill in the trust gap between the principal election layer and the computational layer by a reliable translation of trusted and understandable criteria into trustworthy software. Thereby, we enable that executable procedures can be formally traced back and understood by election experts without the need for inspection on code level, and trust can be preserved to the trustworthy system. The works in this thesis all contribute to this end and consist in five distinct contributions, which are the following: (I) a method for the generation of secure card-based communication schemes, (II) a method for the synthesis of reliable tallying procedures, (III) a method for the efficient verification of reliable tallying procedures, (IV) a method for the computation of dependable election margins for reliable audits, (V) a case study about the security verification of the GI voter-anonymization software. These contributions span formal methods on illustrative examples for each of the three principal components, (1) voter-ballot box communication, (2) election method, and (3) election management, between the election layer and the computational layer. Within the first component, the voter-ballot box communication channel, we build a bridge from the communication channel to the cryptography scheme by automatically generating secure card-based schemes from a small formal model with a parameterization of the desired security requirements. For the second component, the election method, we build a bridge from the election method to the tallying procedure by (1) automatically synthesizing a runnable tallying procedure from the desired requirements given as properties that capture the desired intuitions or regulations of fairness considerations, (2) automatically generating either comprehensible arguments or bounded proofs to compare tallying procedures based on user-definable fairness properties, and (3) automatically computing concrete election margins for a given tallying procedure, the collected ballots, and the computed election result, that enable efficient election audits. Finally, for the third and final component, the election management system, we perform a case study and apply state-of-the-art verification technology to a real-world e-voting system that has been used for the annual elections of the German Informatics Society (GI – “Gesellschaft für Informatik”) in 2019. The case study consists in the formal implementation-level security verification that the voter identities are securely anonymized and the voters’ passwords cannot be leaked. The presented methods assist the systematic development and verification of provenly trustworthy voting systems across traditional layers, i.e., from the election layer to the computational layer. They all pursue the goal of making voting systems trustworthy by reliable and explainable formal requirements. We evaluate the devised methods on minimal card-based protocols that compute a secure AND function for two different decks of cards, a classical knock-out tournament and several Condorcet rules, various plurality, scoring, and Condorcet rules from the literature, the Danish national parliamentary elections in 2015, and a state-of-the-art electronic voting system that is used for the German Informatics Society’s annual elections in 2019 and following
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