Instrumental Music Learning in an Irish Bimusical Context

This study focuses on bimusical instrumental learning, exploring the perceptions, beliefs and musical practices of students who are simultaneously engaged in learning classical and Irish traditional musics. The literature on bimusicality addresses how it has evolved in various social and educational contexts. This research focuses on the bimusical learning processes and practices of students, aged sixteen to twenty years, as they cross between the different learning modes associated with these two musical traditions in an Irish context. This qualitative study adopts a collective case study approach, using a purposive sampling strategy. Data collected include: videotaped lessons, recorded practice/playing sessions, observations of a range of music-making activities, and interviews with the participants, their parents and teachers. The seven participants were chosen from various formal and informal learning contexts and represent a range of instruments: a saxophonist/traditional flute/uilleann piper; two violinist/fiddlers; a cellist/uilleann pipes/whistle player; a classical/traditional harpist/concertina player; a pianist/flute player; and a pianist/accordion player. The research findings highlight the individuality of these students’ bimusical practices and are suggestive of a more nuanced image of the natural bimusical musician than was perhaps indicated in earlier literature. There is evidence of different levels of immersion, participation, commitment and, to some extent, fluency in the participants’ involvement in the two traditions. The research illustrates how issues such as diversity, choice, ease and ownership are important to these students as they sustain their musical involvements in both traditions. The communal/social dimension of music making receives special attention, particularly in the context of group music making. Such concepts as tradition, innovation and identity also emerge as the thesis explores how these young musicians negotiate the many similarities, confluences and contrasts of their individual bimusical worlds

Insiders\u27 Guide to the Student Academic Conference: 11th Annual SAC

Minnesota State University Moorhead Student Academic Conference abstract book

Singing poets: popular music and literature in France and Greece (1945-1975).

This thesis is based on a comparative examination of popular music in Greece and France between 1945 and 1975. Its central claim is that the concept of the singing poet provided a crucial framing of the field of popular music in both countries and led to a reassessment of the links between literature and popular culture. The term singing poets is coined in order to regroup artists who used poetic texts for their songs or adopted a poetic persona themselves, but also accounts for the reception of a particular style of popular music in the period and the countries under discussion as poetic/intellectual song. Adopting a Cultural Studies approach, this thesis thus outlines the role played by the prestige of literary institutions and an idealized view of oral poetry in the conceptualization of high-popular music. It questions the presentation of certain singersongwriters as 'poets in their own right', as folk poets, auteurs, poet-composers, bards and troubadours. Books, special editions and articles published in France in the 60s are extensively examined in the first part to reveal their traditionalist consensus about the poetic value of the work of certain Auteurs-Compositeurs-Interprétes. Roland Barthes's theorization of reading (and) jouissance provides a vivid counterargument by opening up the possibility of seeing literariness and pop pleasure as symbiotic rather than mutually exclusive. The second part focuses on Greek popular music and reviews how the field of what was termed Entehno Laiko (Art-Popular) has been performatively shaped by the work of Mikis Theodorakis and Manos Hadjidakis. The significant input of literary ideals and the success of Theodorakis's Melopoiemene Poiese (Sung Poetry) project are fundamental to this process. The resulting cultural divide between 'high' and 'low' popular music spheres is reassessed by examining the 'dislocating' performance of singer-songwriter Dionysis Savvopoulos, who appeared in the mid-60s performing a hybrid mimicry of Georges Brassens and Bob Dylan. Through readings of his songs, performances and interviews, popular music emerges both as the space of a reconstructed utopia and as a subversive Other to high cultural forms

Quantifying and Enhancing the Security of Federated Learning

Federated learning is an emerging distributed learning paradigm that allows multiple users to collaboratively train a joint machine learning model without having to share their private data with any third party. Due to many of its attractive properties, federated learning has received significant attention from academia as well as industry and now powers major applications, e.g., Google\u27s Gboard and Assistant, Apple\u27s Siri, Owkin\u27s health diagnostics, etc. However, federated learning is yet to see widespread adoption due to a number of challenges. One such challenge is its susceptibility to poisoning by malicious users who aim to manipulate the joint machine learning model. In this work, we take significant steps towards this challenge. We start by providing a systemization of poisoning adversaries in federated learning and use it to build adversaries with varying strengths and to show how some adversaries common in the prior literature are not practically relevant. For the majority of this thesis, we focus on untargeted poisoning as it can impact much larger federated learning population than other types of poisoning and also because most of the prior poisoning defenses for federated learning aim to defend against untargeted poisoning. % Next, we introduce a general framework to design strong untargeted poisoning attacks against various federated learning algorithms. Using our framework, we design state-of-the-art poisoning attacks and demonstrate how the theoretical guarantees and empirical claims of prior state-of-the-art federated learning poisoning defenses are brittle under the same strong (albeit theoretical) adversaries that these defenses aim to defend against. We also provide concrete lessons highlighting the shortcomings of prior defenses. Using these lessons, we also design two novel defenses with strong theoretical guarantees and demonstrate their state-of-the-art performances in various adversarial settings. Finally, for the first time, we thoroughly investigate the impact of poisoning in real-world federated learning settings and draw significant, and rather surprising, conclusions about robustness of federated learning in practice. For instance, we show that contrary to the established belief, federated learning is highly robust in practice even when using simple, low-cost defenses. One of the major implications of our study is that, although interesting from theoretical perspectives, many of the strong adversaries, and hence, strong prior defenses, are of little use in practice

iCORPP: Interleaved Commonsense Reasoning and Probabilistic Planning on Robots

Robot sequential decision-making in the real world is a challenge because it requires the robots to simultaneously reason about the current world state and dynamics, while planning actions to accomplish complex tasks. On the one hand, declarative languages and reasoning algorithms well support representing and reasoning with commonsense knowledge. But these algorithms are not good at planning actions toward maximizing cumulative reward over a long, unspecified horizon. On the other hand, probabilistic planning frameworks, such as Markov decision processes (MDPs) and partially observable MDPs (POMDPs), well support planning to achieve long-term goals under uncertainty. But they are ill-equipped to represent or reason about knowledge that is not directly related to actions. In this article, we present a novel algorithm, called iCORPP, to simultaneously estimate the current world state, reason about world dynamics, and construct task-oriented controllers. In this process, robot decision-making problems are decomposed into two interdependent (smaller) subproblems that focus on reasoning to "understand the world" and planning to "achieve the goal" respectively. Contextual knowledge is represented in the reasoning component, which makes the planning component epistemic and enables active information gathering. The developed algorithm has been implemented and evaluated both in simulation and on real robots using everyday service tasks, such as indoor navigation, dialog management, and object delivery. Results show significant improvements in scalability, efficiency, and adaptiveness, compared to competitive baselines including handcrafted action policies