597 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    The University of Montana: A History Through the Lens of Physical Culture, PE, Health, Athletics, and Recreation 1897-2019: The Evolution of a Department

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    https://scholarworks.umt.edu/burns/1000/thumbnail.jp

    Trajectories of Change, from Armed Struggle to Politics: The Transformation of Sudan People’s Liberation Movement (SPLM) from a Liberation Movement into a Political Party

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    The end of the Cold War catalysed a range of civil wars and separatist conflicts that battled for government control around the globe. Most of them were resolved through peace agreements which led rebels to lay down their arms and adopt political strategies to pursue their goals. A primary challenge for any resistance or liberation movement is how to win legitimacy and support from the population. This thesis is a case study on the transformation of the Sudan People Liberation Movement/Army (SPLM/A) from a liberation movement to a political party and, later, government. It provides a context-specific understanding and analysis of how the liberation movement garnered legitimacy by tapping into local and international support in the liberation war. The analysis uses legitimacy as the optic for exploring the historical narrative and process-tracing to unearth multifaceted and interactive mechanisms, and strategies facilitating the liberation movement’s quest to consolidate domestic and international legitimacy during the period of struggle. The study employs a theoretical framework focusing on the concept of legitimacy as developed by Max Weber and other scholars. The theoretical approach expands the application of the term ‘legitimacy’ by including concepts such as revolutionary ideology, and performance, or eudaemonic legitimacy. Revolutionary ideology plays a vital role in helping a liberation movement to garner support and political legitimacy from the population during a conflict. It also arises through the invocation of universal values such as freedom, equality, and social justice democracy. Equally important is performance or eudaemonic legitimacy, which is measured by the ability of a former liberation movement to fulfil its revolutionary promises in the aftermath of (violent) conflict. Such a process entails the fulfilment and deliverance of ideals of liberation earlier promised during a struggle period. The promises may include the provision of security, public goods, and welfare to the citizens. However, in comparison to motives, objectives and aspirations of the SPLM/A during the liberation war against the central government in Khartoum, key findings on SPLM/A’s trajectory from a rebel movement to a government in the post-conflict period are not encouraging. The optimism, the hard-won jubilation, and the revolutionary legitimacy that catapulted the SPLM/A to power and the subsequent secession and independence in July 2011 quickly began to wane. The study found that SPLM/A’s legitimacy in the post-CPA and independence period continues to decline, and the South Sudanese do not enjoy the fruits of the liberation struggle. The findings also indicate that the SPLM/A is stuck in a political limbo: it retains many traits of a liberation movement, while its free ride during the CPA-mandated interim period en route to forming South Sudan’s first government has in effect worked against its aspiration to transform into a legitimate political party

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    Blockchain technology: Disruptor or enhancer to the accounting and auditing profession

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    The unique features of blockchain technology (BCT) - peer-to-peer network, distribution ledger, consensus decision-making, transparency, immutability, auditability, and cryptographic security - coupled with the success enjoyed by Bitcoin and other cryptocurrencies have encouraged many to assume that the technology would revolutionise virtually all aspects of business. A growing body of scholarship suggests that BCT would disrupt the accounting and auditing fields by changing accounting practices, disintermediating auditors, and eliminating financial fraud. BCT disrupts audits (Lombard et al.,2021), reduces the role of audit firms (Yermack 2017), undermines accountants' roles with software developers and miners (Fortin & Pimentel 2022); eliminates many management functions, transforms businesses (Tapscott & Tapscott, 2017), facilitates a triple-entry accounting system (Cai, 2021), and prevents fraudulent transactions (Dai, et al., 2017; Rakshit et al., 2022). Despite these speculations, scholars have acknowledged that the application of BCT in the accounting and assurance industry is underexplored and many existing studies are said to lack engagement with practitioners (Dai & Vasarhelyi, 2017; Lombardi et al., 2021; Schmitz & Leoni, 2019). This study empirically explored whether BCT disrupts or enhances accounting and auditing fields. It also explored the relevance of audit in a BCT environment and the effectiveness of the BCT mechanism for fraud prevention and detection. The study further examined which technical skillsets accountants and auditors require in a BCT environment, and explored the incentives, barriers, and unintended consequences of the adoption of BCT in the accounting and auditing professions. The current COVID-19 environment was also investigated in terms of whether the pandemic has improved BCT adoption or not. A qualitative exploratory study used semi-structured interviews to engage practitioners from blockchain start-ups, IT experts, financial analysts, accountants, auditors, academics, organisational leaders, consultants, and editors who understood the technology. With the aid of NVIVO qualitative analysis software, the views of 44 participants from 13 countries: New Zealand, Australia, United States, United Kingdom, Canada, Germany, Italy, Ireland, Hong Kong, India, Pakistan, United Arab Emirates, and South Africa were analysed. The Technological, Organisational, and Environmental (TOE) framework with consequences of innovation context was adopted for this study. This expanded TOE framework was used as the theoretical lens to understand the disruption of BCT and its adoption in the accounting and auditing fields. Four clear patterns emerged. First, BCT is an emerging tool that accountants and auditors use mainly to analyse financial records because technology cannot disintermediate auditors from the financial system. Second, the technology can detect anomalies but cannot prevent financial fraud. Third, BCT has not been adopted by any organisation for financial reporting and accounting purposes, and accountants and auditors do not require new skillsets or an understanding of the BCT programming language to be able to operate in a BCT domain. Fourth, the advent of COVID-19 has not substantially enhanced the adoption of BCT. Additionally, this study highlights the incentives, barriers, and unintended consequences of adopting BCT as financial technology (FinTech). These findings shed light on important questions about BCT disrupting and disintermediating auditors, the extent of adoption in the accounting industry, preventing fraud and anomalies, and underscores the notion that blockchain, as an emerging technology, currently does not appear to be substantially disrupting the accounting and auditing profession. This study makes methodological, theoretical, and practical contributions. At the methodological level, the study adopted the social constructivist-interpretivism paradigm with an exploratory qualitative method to engage and understand BCT as a disruptive innovation in the accounting industry. The engagement with practitioners from diverse fields, professions, and different countries provides a distinctive and innovative contribution to methodological and practical knowledge. At the theoretical level, the findings contribute to the literature by offering an integrated conceptual TOE framework. The framework offers a reference for practitioners, academics and policymakers seeking to appraise comprehensive factors influencing BCT adoption and its likely unintended consequences. The findings suggest that, at present, no organisations are using BCT for financial reporting and accounting systems. This study contributes to practice by highlighting the differences between initial expectations and practical applications of what BCT can do in the accounting and auditing fields. The study could not find any empirical evidence that BCT will disrupt audits, eliminate the roles of auditors in a financial system, and prevent and detect financial fraud. Also, there was no significant evidence that accountants and auditors required higher-level skillsets and an understanding of BCT programming language to be able to use the technology. Future research should consider the implications of an external audit firm as a node in a BCT network on the internal audit functions. It is equally important to critically examine the relevance of including programming languages or codes in the curriculum of undergraduate accounting students. Future research could also empirically evaluate if a BCT enabled triple-entry system could prevent financial statements and management fraud

    Blockchain Technology: Disruptor or Enhnancer to the Accounting and Auditing Profession

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    The unique features of blockchain technology (BCT) - peer-to-peer network, distribution ledger, consensus decision-making, transparency, immutability, auditability, and cryptographic security - coupled with the success enjoyed by Bitcoin and other cryptocurrencies have encouraged many to assume that the technology would revolutionise virtually all aspects of business. A growing body of scholarship suggests that BCT would disrupt the accounting and auditing fields by changing accounting practices, disintermediating auditors, and eliminating financial fraud. BCT disrupts audits (Lombard et al.,2021), reduces the role of audit firms (Yermack 2017), undermines accountants' roles with software developers and miners (Fortin & Pimentel 2022); eliminates many management functions, transforms businesses (Tapscott & Tapscott, 2017), facilitates a triple-entry accounting system (Cai, 2021), and prevents fraudulent transactions (Dai, et al., 2017; Rakshit et al., 2022). Despite these speculations, scholars have acknowledged that the application of BCT in the accounting and assurance industry is underexplored and many existing studies are said to lack engagement with practitioners (Dai & Vasarhelyi, 2017; Lombardi et al., 2021; Schmitz & Leoni, 2019). This study empirically explored whether BCT disrupts or enhances accounting and auditing fields. It also explored the relevance of audit in a BCT environment and the effectiveness of the BCT mechanism for fraud prevention and detection. The study further examined which technical skillsets accountants and auditors require in a BCT environment, and explored the incentives, barriers, and unintended consequences of the adoption of BCT in the accounting and auditing professions. The current COVID-19 environment was also investigated in terms of whether the pandemic has improved BCT adoption or not. A qualitative exploratory study used semi-structured interviews to engage practitioners from blockchain start-ups, IT experts, financial analysts, accountants, auditors, academics, organisational leaders, consultants, and editors who understood the technology. With the aid of NVIVO qualitative analysis software, the views of 44 participants from 13 countries: New Zealand, Australia, United States, United Kingdom, Canada, Germany, Italy, Ireland, Hong Kong, India, Pakistan, United Arab Emirates, and South Africa were analysed. The Technological, Organisational, and Environmental (TOE) framework with consequences of innovation context was adopted for this study. This expanded TOE framework was used as the theoretical lens to understand the disruption of BCT and its adoption in the accounting and auditing fields. Four clear patterns emerged. First, BCT is an emerging tool that accountants and auditors use mainly to analyse financial records because technology cannot disintermediate auditors from the financial system. Second, the technology can detect anomalies but cannot prevent financial fraud. Third, BCT has not been adopted by any organisation for financial reporting and accounting purposes, and accountants and auditors do not require new skillsets or an understanding of the BCT programming language to be able to operate in a BCT domain. Fourth, the advent of COVID-19 has not substantially enhanced the adoption of BCT. Additionally, this study highlights the incentives, barriers, and unintended consequences of adopting BCT as financial technology (FinTech). These findings shed light on important questions about BCT disrupting and disintermediating auditors, the extent of adoption in the accounting industry, preventing fraud and anomalies, and underscores the notion that blockchain, as an emerging technology, currently does not appear to be substantially disrupting the accounting and auditing profession. This study makes methodological, theoretical, and practical contributions. At the methodological level, the study adopted the social constructivist-interpretivism paradigm with an exploratory qualitative method to engage and understand BCT as a disruptive innovation in the accounting industry. The engagement with practitioners from diverse fields, professions, and different countries provides a distinctive and innovative contribution to methodological and practical knowledge. At the theoretical level, the findings contribute to the literature by offering an integrated conceptual TOE framework. The framework offers a reference for practitioners, academics and policymakers seeking to appraise comprehensive factors influencing BCT adoption and its likely unintended consequences. The findings suggest that, at present, no organisations are using BCT for financial reporting and accounting systems. This study contributes to practice by highlighting the differences between initial expectations and practical applications of what BCT can do in the accounting and auditing fields. The study could not find any empirical evidence that BCT will disrupt audits, eliminate the roles of auditors in a financial system, and prevent and detect financial fraud. Also, there was no significant evidence that accountants and auditors required higher-level skillsets and an understanding of BCT programming language to be able to use the technology. Future research should consider the implications of an external audit firm as a node in a BCT network on the internal audit functions. It is equally important to critically examine the relevance of including programming languages or codes in the curriculum of undergraduate accounting students. Future research could also empirically evaluate if a BCT-enabled triple-entry system could prevent financial statements and management fraud

    Nesting optimization with adversarial games, meta-learning, and deep equilibrium models

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    Nested optimization, whereby an optimization problem is constrained by the solutions of other optimization problems, has recently seen a surge in its application to Deep Learning. While the study of such problems started nearly a century ago in the context of market theory, many of the algorithms developed since do not scale to modern Deep Learning applications. In this thesis, I push the understanding and applicability of nested optimization to three machine learning domains: 1) adversarial games, 2) meta-learning and 3) deep equilibrium models. For each domain, I tackle a particular goal. In 1) I adversarially learn model compression, in the case where training data isn't available, in 2) I meta-learn hyperparameters for long optimization processes without introducing greediness, and in 3) I use deep equilibrium models to improve temporal coherence in video landmark detection. The first part of my thesis deals with casting model compression as an adversarial game. Performing knowledge transfer from a large teacher network to a smaller student is a popular task in deep learning. However, due to growing dataset sizes and stricter privacy regulations, it is increasingly common not to have access to the data that was used to train the teacher. I propose a novel method which trains a student to match the predictions of its teacher without using any data or metadata. This is achieved by nesting the training optimization of the student with that of an adversarial generator, which searches for images on which the student poorly matches the teacher. These images are used to train the student in an online fashion. The student closely approximates its teacher for simple datasets like SVHN, and on CIFAR10 I improve on the state-of-the-art for few-shot distillation (with 100100 images per class), despite using no data. Finally, I also propose a metric to quantify the degree of belief matching between teacher and student in the vicinity of decision boundaries, and observe a significantly higher match between the zero-shot student and the teacher, than between a student distilled with real data and the teacher. The second part of my thesis deals with meta-learning hyperparameters in the case when the nested optimization to be differentiated is itself solved by many gradient steps. Gradient-based hyperparameter optimization has earned a widespread popularity in the context of few-shot meta-learning, but remains broadly impractical for tasks with long horizons (many gradient steps), due to memory scaling and gradient degradation issues. A common workaround is to learn hyperparameters online, but this introduces greediness which comes with a significant performance drop. I propose forward-mode differentiation with sharing (FDS), a simple and efficient algorithm which tackles memory scaling issues with forward-mode differentiation, and gradient degradation issues by sharing hyperparameters that are contiguous in time. I provide theoretical guarantees about the noise reduction properties of my algorithm, and demonstrate its efficiency empirically by differentiating through 104\sim 10^4 gradient steps of unrolled optimization. I consider large hyperparameter search ranges on CIFAR-10 where I significantly outperform greedy gradient-based alternatives, while achieving ×20\times 20 speedups compared to the state-of-the-art black-box methods. The third part of my thesis deals with converting deep equilibrium models to a form of nested optimization in order to perform robust video landmark detection. Cascaded computation, whereby predictions are recurrently refined over several stages, has been a persistent theme throughout the development of landmark detection models. I show that the recently proposed deep equilibrium model (DEQ) can be naturally adapted to this form of computation, given appropriate regularization. My landmark model achieves state-of-the-art performance on the challenging WFLW facial landmark dataset, reaching 3.923.92 normalized mean error with fewer parameters and a training memory cost of O(1)\mathcal{O}(1) in the number of recurrent modules. Furthermore, I show that DEQs are particularly suited for landmark detection in videos. In this setting, it is typical to train on still images due to the lack of labeled videos. This can lead to a ``flickering'' effect at inference time on video, whereby a model can rapidly oscillate between different plausible solutions across consecutive frames. I show that the DEQ root solving problem can be turned into a constrained optimization problem in a way that emulates recurrence at inference time, despite not having access to temporal data at training time. I call this "Recurrence without Recurrence'', and demonstrate that it helps reduce landmark flicker by introducing a new metric, and contributing a new facial landmark video dataset targeting landmark uncertainty. On the hard subset of this new dataset, made up of 500500 videos, my model improves the accuracy and temporal coherence by 1010 and 13%13\% respectively, compared to the strongest previously published model using a hand-tuned conventional filter

    LIPIcs, Volume 274, ESA 2023, Complete Volume

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    LIPIcs, Volume 274, ESA 2023, Complete Volum
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