DSpace Coleção:https://repositorio.pucsp.br/jspui/handle/handle/180502024-03-24T10:11:34Z2024-03-24T10:11:34ZAprendizado de máquina aplicado à pedagogia da improvisação musicalhttps://repositorio.pucsp.br/jspui/handle/handle/408642024-02-05T15:44:49Z2023-11-10T00:00:00ZTítulo: Aprendizado de máquina aplicado à pedagogia da improvisação musical
Abstract: Musical improvisation has been performed by programable machines for a long time, which reinforces the question about the role technology plays in culture and how it could better assist humans in complex topics and open domains. It’s possible to verify a number of artificial intelligence-based applications, with technology and performance on the spot while literature relating AI aspects to musical improvisation pedagogy in popular music is scarse. This research explores how machine learning aspects, as features engineering, could better assist the human cognition in such complex topic as the musical improvisation, using machine learning techniques to explicit idiomatic improvisational rules found in the literature from a digital database containing transcribed recordings. Even though relationships have been found, there are a number of parameters to be considered in order to encompass all the complexity behind this subject.
Tipo: Dissertação2023-11-10T00:00:00ZO cérebro eletrônico que me dá socorro: os impactos da Inteligência Artificial Generativa e os usos do ChatGPT na educaçãohttps://repositorio.pucsp.br/jspui/handle/handle/407742024-02-06T12:01:15Z2023-10-30T00:00:00ZTítulo: O cérebro eletrônico que me dá socorro: os impactos da Inteligência Artificial Generativa e os usos do ChatGPT na educação
Abstract: Generative Artificial Intelligence has increasingly gained prominence in various areas of analysis, and consequently, in perspectives regarding its advancements, uses, and future. Therefore, this research aims to investigate how ChatGPT is being used by students in the fields of Consumer Sciences, Film and Audiovisual, Communication and Advertising, and Information Systems at the Escola Superior de Propaganda e Marketing of São Paulo (ESPM-SP). Additionally, the study aims to understand how the use of these technologies can impact the way this audience learns new content. The results presented here are based on a conclusive, descriptive, and unique cross-sectional research approach. The study draws on an understanding of human learning, an overview of the development of Artificial Intelligence, and its application in the field of education, considering its uses, potential future, and challenges
Tipo: Tese2023-10-30T00:00:00ZA proposta do metaverso como uma nova forma socialhttps://repositorio.pucsp.br/jspui/handle/handle/407392024-02-05T15:03:02Z2023-12-15T00:00:00ZTítulo: A proposta do metaverso como uma nova forma social
Abstract: The objective of this work is to analyze the different modalities and perspectives related to the concept of Metaverse, with a focus on the areas of gaming, social networks, design, and entertainment. The intention is to understand the evolution and current characteristics of the Metaverse, which is an extension of the virtual universe where participants interact through digital avatars, recreating real-world experiences. Although the concept of the Metaverse is widely recognized and sparks interest among researchers and professionals from various fields, its technical applications and prospects for innovation and development still lack a deeper analysis. The study seeks to comprehend the current technological limitations and outline potential development trajectories to shape its future. The work will include a case study of Meta (formerly known as Facebook), which is a prominent player in the technological and media scene related to the Metaverse. Choosing this company as the subject of study will enable a broader understanding of the practices and strategies adopted in this context
Tipo: Tese2023-12-15T00:00:00ZDetecção de fake news em redes sociais com o uso de redes neurais recorrentes, redes neurais gráficas e transformershttps://repositorio.pucsp.br/jspui/handle/handle/399992023-11-24T04:04:41Z2023-09-29T00:00:00ZTítulo: Detecção de fake news em redes sociais com o uso de redes neurais recorrentes, redes neurais gráficas e transformers
Abstract: Throughout history, the use of fake news with the intention of deceiving, promoting product sales, and influencing opinions has been there. With the advent of social media, the environment has become conducive to the dissemination of these fraudulent news stories, due to the intersection between stimuli and sharing, the popularization of content production without supervision, and the personalization achieved through the analysis of large volumes of data using advanced modeling techniques. In this context, traditional approaches to dealing with disinformation become insufficient. Based on recent research in the fields of neural networks and behavior, we seek to demonstrate that it is possible to identify and understand the circulation of fake news on social media using deep learning algorithms. This endeavor requires an understanding of different processes of signification, necessitating a theoretical framework that enables us to comprehend the nature and dynamics of these processes. A suitable foundation for developing this understanding is found in C. S. Peirce's theory of signs, in which processes of signification are conceptualized as semiosis, an irreducible and processual triadic relationship between signs, objects, and interpretants. Therefore, this study addresses the application of different neural network architectures, such as recurrent, graph, and transformer networks, for detecting fake news. Furthermore, it aims at understanding whether graph-based neural network models can more effectively capture the communication structure among social media users and be less susceptible to modifications of the writing patterns of fake news. Finally, the study examines whether a combination of linguistic and graph-based models may be more efficient than their isolated use in combating fake news
Tipo: Dissertação2023-09-29T00:00:00Z