812 research outputs found

    2017 GREAT Day Program

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    SUNY Geneseo’s Eleventh Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1011/thumbnail.jp

    The European Experience: A Multi-Perspective History of Modern Europe, 1500–2000

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    The European Experience brings together the expertise of nearly a hundred historians from eight European universities to internationalise and diversify the study of modern European history, exploring a grand sweep of time from 1500 to 2000. Offering a valuable corrective to the Anglocentric narratives of previous English-language textbooks, scholars from all over Europe have pooled their knowledge on comparative themes such as identities, cultural encounters, power and citizenship, and economic development to reflect the complexity and heterogeneous nature of the European experience. Rather than another grand narrative, the international author teams offer a multifaceted and rich perspective on the history of the continent of the past 500 years. Each major theme is dissected through three chronological sub-chapters, revealing how major social, political and historical trends manifested themselves in different European settings during the early modern (1500–1800), modern (1800–1900) and contemporary period (1900–2000). This resource is of utmost relevance to today’s history students in the light of ongoing internationalisation strategies for higher education curricula, as it delivers one of the first multi-perspective and truly ‘European’ analyses of the continent’s past. Beyond the provision of historical content, this textbook equips students with the intellectual tools to interrogate prevailing accounts of European history, and enables them to seek out additional perspectives in a bid to further enrich the discipline

    Data ethics : building trust : how digital technologies can serve humanity

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    Data is the magic word of the 21st century. As oil in the 20th century and electricity in the 19th century: For citizens, data means support in daily life in almost all activities, from watch to laptop, from kitchen to car, from mobile phone to politics. For business and politics, data means power, dominance, winning the race. Data can be used for good and bad, for services and hacking, for medicine and arms race. How can we build trust in this complex and ambiguous data world? How can digital technologies serve humanity? The 45 articles in this book represent a broad range of ethical reflections and recommendations in eight sections: a) Values, Trust and Law, b) AI, Robots and Humans, c) Health and Neuroscience, d) Religions for Digital Justice, e) Farming, Business, Finance, f) Security, War, Peace, g) Data Governance, Geopolitics, h) Media, Education, Communication. The authors and institutions come from all continents. The book serves as reading material for teachers, students, policy makers, politicians, business, hospitals, NGOs and religious organisations alike. It is an invitation for dialogue, debate and building trust! The book is a continuation of the volume “Cyber Ethics 4.0” published in 2018 by the same editors

    Imagining & Sensing: Understanding and Extending the Vocalist-Voice Relationship Through Biosignal Feedback

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    The voice is body and instrument. Third-person interpretation of the voice by listeners, vocal teachers, and digital agents is centred largely around audio feedback. For a vocalist, physical feedback from within the body provides an additional interaction. The vocalist’s understanding of their multi-sensory experiences is through tacit knowledge of the body. This knowledge is difficult to articulate, yet awareness and control of the body are innate. In the ever-increasing emergence of technology which quantifies or interprets physiological processes, we must remain conscious also of embodiment and human perception of these processes. Focusing on the vocalist-voice relationship, this thesis expands knowledge of human interaction and how technology influences our perception of our bodies. To unite these different perspectives in the vocal context, I draw on mixed methods from cog- nitive science, psychology, music information retrieval, and interactive system design. Objective methods such as vocal audio analysis provide a third-person observation. Subjective practices such as micro-phenomenology capture the experiential, first-person perspectives of the vocalists them- selves. Quantitative-qualitative blend provides details not only on novel interaction, but also an understanding of how technology influences existing understanding of the body. I worked with vocalists to understand how they use their voice through abstract representations, use mental imagery to adapt to altered auditory feedback, and teach fundamental practice to others. Vocalists use multi-modal imagery, for instance understanding physical sensations through auditory sensations. The understanding of the voice exists in a pre-linguistic representation which draws on embodied knowledge and lived experience from outside contexts. I developed a novel vocal interaction method which uses measurement of laryngeal muscular activations through surface electromyography. Biofeedback was presented to vocalists through soni- fication. Acting as an indicator of vocal activity for both conscious and unconscious gestures, this feedback allowed vocalists to explore their movement through sound. This formed new perceptions but also questioned existing understanding of the body. The thesis also uncovers ways in which vocalists are in control and controlled by, work with and against their bodies, and feel as a single entity at times and totally separate entities at others. I conclude this thesis by demonstrating a nuanced account of human interaction and perception of the body through vocal practice, as an example of how technological intervention enables exploration and influence over embodied understanding. This further highlights the need for understanding of the human experience in embodied interaction, rather than solely on digital interpretation, when introducing technology into these relationships

    If interpretability is the answer, what is the question?

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    Due to the ability to model even complex dependencies, machine learning (ML) can be used to tackle a broad range of (high-stakes) prediction problems. The complexity of the resulting models comes at the cost of transparency, meaning that it is difficult to understand the model by inspecting its parameters. This opacity is considered problematic since it hampers the transfer of knowledge from the model, undermines the agency of individuals affected by algorithmic decisions, and makes it more challenging to expose non-robust or unethical behaviour. To tackle the opacity of ML models, the field of interpretable machine learning (IML) has emerged. The field is motivated by the idea that if we could understand the model's behaviour -- either by making the model itself interpretable or by inspecting post-hoc explanations -- we could also expose unethical and non-robust behaviour, learn about the data generating process, and restore the agency of affected individuals. IML is not only a highly active area of research, but the developed techniques are also widely applied in both industry and the sciences. Despite the popularity of IML, the field faces fundamental criticism, questioning whether IML actually helps in tackling the aforementioned problems of ML and even whether it should be a field of research in the first place: First and foremost, IML is criticised for lacking a clear goal and, thus, a clear definition of what it means for a model to be interpretable. On a similar note, the meaning of existing methods is often unclear, and thus they may be misunderstood or even misused to hide unethical behaviour. Moreover, estimating conditional-sampling-based techniques poses a significant computational challenge. With the contributions included in this thesis, we tackle these three challenges for IML. We join a range of work by arguing that the field struggles to define and evaluate "interpretability" because incoherent interpretation goals are conflated. However, the different goals can be disentangled such that coherent requirements can inform the derivation of the respective target estimands. We demonstrate this with the examples of two interpretation contexts: recourse and scientific inference. To tackle the misinterpretation of IML methods, we suggest deriving formal interpretation rules that link explanations to aspects of the model and data. In our work, we specifically focus on interpreting feature importance. Furthermore, we collect interpretation pitfalls and communicate them to a broader audience. To efficiently estimate conditional-sampling-based interpretation techniques, we propose two methods that leverage the dependence structure in the data to simplify the estimation problems for Conditional Feature Importance (CFI) and SAGE. A causal perspective proved to be vital in tackling the challenges: First, since IML problems such as algorithmic recourse are inherently causal; Second, since causality helps to disentangle the different aspects of model and data and, therefore, to distinguish the insights that different methods provide; And third, algorithms developed for causal structure learning can be leveraged for the efficient estimation of conditional-sampling based IML methods.Aufgrund der Fähigkeit, selbst komplexe Abhängigkeiten zu modellieren, kann maschinelles Lernen (ML) zur Lösung eines breiten Spektrums von anspruchsvollen Vorhersageproblemen eingesetzt werden. Die Komplexität der resultierenden Modelle geht auf Kosten der Interpretierbarkeit, d. h. es ist schwierig, das Modell durch die Untersuchung seiner Parameter zu verstehen. Diese Undurchsichtigkeit wird als problematisch angesehen, da sie den Wissenstransfer aus dem Modell behindert, sie die Handlungsfähigkeit von Personen, die von algorithmischen Entscheidungen betroffen sind, untergräbt und sie es schwieriger macht, nicht robustes oder unethisches Verhalten aufzudecken. Um die Undurchsichtigkeit von ML-Modellen anzugehen, hat sich das Feld des interpretierbaren maschinellen Lernens (IML) entwickelt. Dieses Feld ist von der Idee motiviert, dass wir, wenn wir das Verhalten des Modells verstehen könnten - entweder indem wir das Modell selbst interpretierbar machen oder anhand von post-hoc Erklärungen - auch unethisches und nicht robustes Verhalten aufdecken, über den datengenerierenden Prozess lernen und die Handlungsfähigkeit betroffener Personen wiederherstellen könnten. IML ist nicht nur ein sehr aktiver Forschungsbereich, sondern die entwickelten Techniken werden auch weitgehend in der Industrie und den Wissenschaften angewendet. Trotz der Popularität von IML ist das Feld mit fundamentaler Kritik konfrontiert, die in Frage stellt, ob IML tatsächlich dabei hilft, die oben genannten Probleme von ML anzugehen, und ob es überhaupt ein Forschungsgebiet sein sollte: In erster Linie wird an IML kritisiert, dass es an einem klaren Ziel und damit an einer klaren Definition dessen fehlt, was es für ein Modell bedeutet, interpretierbar zu sein. Weiterhin ist die Bedeutung bestehender Methoden oft unklar, so dass sie missverstanden oder sogar missbraucht werden können, um unethisches Verhalten zu verbergen. Letztlich stellt die Schätzung von auf bedingten Stichproben basierenden Verfahren eine erhebliche rechnerische Herausforderung dar. In dieser Arbeit befassen wir uns mit diesen drei grundlegenden Herausforderungen von IML. Wir schließen uns der Argumentation an, dass es schwierig ist, "Interpretierbarkeit" zu definieren und zu bewerten, weil inkohärente Interpretationsziele miteinander vermengt werden. Die verschiedenen Ziele lassen sich jedoch entflechten, sodass kohärente Anforderungen die Ableitung der jeweiligen Zielgrößen informieren. Wir demonstrieren dies am Beispiel von zwei Interpretationskontexten: algorithmischer Regress und wissenschaftliche Inferenz. Um der Fehlinterpretation von IML-Methoden zu begegnen, schlagen wir vor, formale Interpretationsregeln abzuleiten, die Erklärungen mit Aspekten des Modells und der Daten verknüpfen. In unserer Arbeit konzentrieren wir uns speziell auf die Interpretation von sogenannten Feature Importance Methoden. Darüber hinaus tragen wir wichtige Interpretationsfallen zusammen und kommunizieren sie an ein breiteres Publikum. Zur effizienten Schätzung auf bedingten Stichproben basierender Interpretationstechniken schlagen wir zwei Methoden vor, die die Abhängigkeitsstruktur in den Daten nutzen, um die Schätzprobleme für Conditional Feature Importance (CFI) und SAGE zu vereinfachen. Eine kausale Perspektive erwies sich als entscheidend für die Bewältigung der Herausforderungen: Erstens, weil IML-Probleme wie der algorithmische Regress inhärent kausal sind; zweitens, weil Kausalität hilft, die verschiedenen Aspekte von Modell und Daten zu entflechten und somit die Erkenntnisse, die verschiedene Methoden liefern, zu unterscheiden; und drittens können wir Algorithmen, die für das Lernen kausaler Struktur entwickelt wurden, für die effiziente Schätzung von auf bindingten Verteilungen basierenden IML-Methoden verwenden

    From Brave New World to Ready Player One; yesterday's dystopias as tomorrow's utopias

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    The purpose of this thesis is to investigate the evolution of literary utopias and dystopias in light of what I consider to be the new master narrative of post-humanism. The inspiration for this project has been Yuval Noah Harari’s Homo Deus (2017), which details some of the challenges and opportunities we might face in the future. This thesis will therefore explore the themes of his work and try to assess how this technological and ideological development changes our perception of an ideal society. I start this project by clarifying utopia and dystopia, before delving into a history of post-humanist ideology up to the present moment. This thesis then attempts a reading of Brave New World (1932) by Aldous Huxley and Ready Player One (2011) by Ernest Cline. This reading of these two works will be a twofold attempt, where a comparison of traditional liberal humanist readings will be compared to post-humanist understandings. What this hopefully highlights is how post-humanism changes the definitions of what constitutes utopias and dystopias. Finally, I want to compare the two works and hopefully glean new insights about how post-humanism redefines ideal visions and ideal societies and avoiding the dangers of both utopias and dystopias in the post-human age

    A Search for Role Clarity: A Critical Discourse Analysis of the RN and RPN Entry-to-Practice Competencies That Shape Nursing Curriculum in Ontario, Canada

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    There is confusion regarding the practice expectations of Registered Nurses (RN) and Registered Practical Nurses (RPN) for employers, educators, nurses, nursing students, and the public, in Ontario, Canada. As the entry-to-practice competencies (ETPC) serve as a guide to the curricular content of nursing programs, a critical discourse analysis of the entry-to-practice documents available to the public was performed to: 1) attempt to understand the meaning and intent of the ETPC, 2) to answer the question of what are the differences in practice expectations for RN versus RPN graduates, and 3) how can role clarity be improved through this process. Critical discourse analysis affords the opportunity to understand these documents, not just through the words on the page, but understand the social, cultural, political, and contextual forces and processes that led to their creation by the nursing regulators in Canada. However, there are competency interpretation documents available only to nursing educators embarking on the College of Nurses of Ontario (CNO) program approval process, which are not made readily available to nurses, the public or employers. These interpretation documents provide a clearer picture of the key differences and similarities between RN and RPN practice expectations. Despite this increased clarity, some language use and sentence construction confound even a seasoned educator as these words have different meanings depending on the context and common understanding of the meaning. As a useful tool for nursing practice, a table of comparison was made to guide nursing educators, employers, nurses, nursing students, and the public to make visible these differences and similarities in both the competencies and the interpretation documents. This analysis also suggests that the College of Nurses of Ontario make the interpretation documents available to a wider audience to support the link between nursing practice and nursing education to create a living curriculum that can be responsive to the ever-changing needs of the profession

    Ensembles of Pruned Deep Neural Networks for Accurate and Privacy Preservation in IoT Applications

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    The emergence of the AIoT (Artificial Intelligence of Things) represents the powerful convergence of Artificial Intelligence (AI) with the expansive realm of the Internet of Things (IoT). By integrating AI algorithms with the vast network of interconnected IoT devices, we open new doors for intelligent decision-making and edge data analysis, transforming various domains from healthcare and transportation to agriculture and smart cities. However, this integration raises pivotal questions: How can we ensure deep learning models are aptly compressed and quantised to operate seamlessly on devices constrained by computational resources, without compromising accuracy? How can these models be effectively tailored to cope with the challenges of statistical heterogeneity and the uneven distribution of class labels inherent in IoT applications? Furthermore, in an age where data is a currency, how do we uphold the sanctity of privacy for the sensitive data that IoT devices incessantly generate while also ensuring the unhampered deployment of these advanced deep learning models? Addressing these intricate challenges forms the crux of this thesis, with its contributions delineated as follows: Ensyth: A novel approach designed to synthesise pruned ensembles of deep learning models, which not only makes optimal use of limited IoT resources but also ensures a notable boost in predictability. Experimental evidence gathered from CIFAR-10, CIFAR-5, and MNIST-FASHION datasets solidify its merit, especially given its capacity to achieve high predictability. MicroNets: Venturing into the realms of efficiency, this is a multi-phase pruning pipeline that fuses the principles of weight pruning, channel pruning. Its objective is clear: foster efficient deep ensemble learning, specially crafted for IoT devices. Benchmark tests conducted on CIFAR-10 and CIFAR-100 datasets demonstrate its prowess, highlighting a compression ratio of nearly 92%, with these pruned ensembles surpassing the accuracy metrics set by conventional models. FedNets: Recognising the challenges of statistical heterogeneity in federated learning and the ever-growing concerns of data privacy, this innovative federated learning framework is introduced. It facilitates edge devices in their collaborative quest to train ensembles of pruned deep neural networks. More than just training, it ensures data privacy remains uncompromised. Evaluations conducted on the Federated CIFAR-100 dataset offer a testament to its efficacy. In this thesis, substantial contributions have been made to the AIoT application domain. Ensyth, MicroNets, and FedNets collaboratively tackle the challenges of efficiency, accuracy, statistical heterogeneity arising from distributed class labels, and privacy concerns inherent in deploying AI applications on IoT devices. The experimental results underscore the effectiveness of these approaches, paving the way for their practical implementation in real-world scenarios. By offering an integrated solution that satisfies multiple key requirements simultaneously, this research brings us closer to the realisation of effective and privacy-preserved AIoT systems

    Cyberbullying in educational context

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    Kustenmacher and Seiwert (2004) explain a man’s inclination to resort to technology in his interaction with the environment and society. Thus, the solution to the negative consequences of Cyberbullying in a technologically dominated society is represented by technology as part of the technological paradox (Tugui, 2009), in which man has a dual role, both slave and master, in the interaction with it. In this respect, it is noted that, notably after 2010, there have been many attempts to involve artificial intelligence (AI) to recognize, identify, limit or avoid the manifestation of aggressive behaviours of the CBB type. For an overview of the use of artificial intelligence in solving various problems related to CBB, we extracted works from the Scopus database that respond to the criterion of the existence of the words “cyberbullying” and “artificial intelligence” in the Title, Keywords and Abstract. These articles were the subject of the content analysis of the title and, subsequently, only those that are identified as a solution in the process of recognizing, identifying, limiting or avoiding the manifestation of CBB were kept in the following Table where we have these data synthesized and organized by years
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