MULHERES DIGITAIS: DESAFIOS (A SEREM) VENCIDOS NA ACADEMIA PARA EQUIDADE DE FATO

The goal of this article is to broadly discuss being a woman in Sciences and the search for gender equity, especially in the areas of Computing, and Information and Communication Technology - digital woman. In such a context, this article refutes the stereotype of the multitasking woman as the ideal professional, which has caused mental and physical illness. The method used is bibliographic and qualitative research. The article also includes provocative calls to action and conclusions, so that gender equity in academic careers can be truly achieved.O objetivo deste artigo é discutir amplamente o ser mulher na Academia Científica e a busca por equidade de gênero, em especial nas áreas de Computação e de Tecnologia da Informação e Comunicação - a mulher digital. Para isso, o artigo refuta o estereótipo da mulher multitarefa como a profissional ideal, o que tem provocado doenças mentais e físicas. O método utilizado é pesquisa bibliográfica e qualitativa. O artigo também inclui chamadas para ação e conclusões provocadoras, para que a equidade de gênero nas carreiras acadêmicas possa ser atingida de fato.&nbsp

Collaboration-Aware Hit Song Prediction

In a streaming-oriented era, predicting which songs will be successful is a significant challenge for the music industry. Indeed, there are many efforts in determining the driving factors that contribute to a song’s success, and one potential solution could be incorporating artistic collaborations, as it allows for a wider audience reach. Therefore, we propose a multi-perspective approach that includes collaboration between artists as a factor for hit song prediction. Specifically, by combining online data from Billboard and Spotify, we tackle the problem as both classification and hit song placement tasks, applying five different model variants. Our results show that relying only on music-related features is not enough, whereas models that also consider collaboration features produce better results

Temporal Success Analyses in Music Collaboration Networks: Brazilian and Global Scenarios

Collaboration is a part of the music industry and has increased over recent decades; but little do we know about its effects on success and evolution. Our goal is to analyze how success has evolved over collaboration networks and compare its global scenario to a local, thriving one: the Brazilian music industry. Specifically, we build collaboration networks from data collected from Spotify's Global and Brazilian daily charts, analyze them and identify collaboration profiles in such networks. Analyses over their topological characteristics reveal collaboration patterns mapped into four different profiles: Standard, Niche, Ephemeral and Absent, where the two first have a higher level of success. Furthermore, we do deeper by evaluating the temporal evolution of such profiles through case studies: pop and k-pop globally, and pop and forró in Brazil. Overall, our findings emphasize the importance of collaboration profiles in assessing success, and show differences between the global and Brazilian scenarios

Temporal Success Analyses in Music Collaboration Networks: Brazilian and Global Scenarios

Collaboration is a part of the music industry and has increased over recent decades; but little do we know about its effects on success and evolution. Our goal is to analyze how success has evolved over collaboration networks and compare its global scenario to a local, thriving one: the Brazilian music industry. Specifically, we build collaboration networks from data collected from Spotify's Global and Brazilian daily charts, analyze them and identify collaboration profiles in such networks. Analyses over their topological characteristics reveal collaboration patterns mapped into four different profiles: Standard, Niche, Ephemeral and Absent, where the two first have a higher level of success. Furthermore, we do deeper by evaluating the temporal evolution of such profiles through case studies: pop and k-pop globally, and pop and forró in Brazil. Overall, our findings emphasize the importance of collaboration profiles in assessing success, and show differences between the global and Brazilian scenarios

Temporal Success Analyses in Music Collaboration Networks: Brazilian and Global Scenarios

Collaboration is a part of the music industry and has increased over recent decades; but little do we know about its effects on success and evolution. Our goal is to analyze how success has evolved over collaboration networks and compare its global scenario to a local, thriving one: the Brazilian music industry. Specifically, we build collaboration networks from data collected from Spotify's Global and Brazilian daily charts, analyze them and identify collaboration profiles in such networks. Analyses over their topological characteristics reveal collaboration patterns mapped into four different profiles: Standard, Niche, Ephemeral and Absent, where the two first have a higher level of success. Furthermore, we do deeper by evaluating the temporal evolution of such profiles through case studies: pop and k-pop globally, and pop and forró in Brazil. Overall, our findings emphasize the importance of collaboration profiles in assessing success, and show differences between the global and Brazilian scenarios

From Compact Discs to Streaming: A Comparison of Eras within the Brazilian Market

The music industry has undergone many changes in the last few decades, notably since vinyl, cassettes and compact discs faded away as streaming platforms took the world by storm. This Digital evolution has made huge volumes of data about music consumption available. Based on such data, we perform cross-era comparisons between Physical and Digital media within the music market in Brazil. First, we build artists' success time series to detect and characterize hot streak periods, defined as high-impact bursts that occur in sequence, in both eras. Then, we identify groups of artists with distinct success levels by applying a cluster analysis based on hot streaks' features. We find the same clusters for both Physical and Digital eras: Spike Hit Artists, Big Hit Artists, and Top Hit Artists. Our results reveal significant changes in the music industry dynamics over the years by identifying the core of each era

From Compact Discs to Streaming: A Comparison of Eras within the Brazilian Market

The music industry has undergone many changes in the last few decades, notably since vinyl, cassettes and compact discs faded away as streaming platforms took the world by storm. This Digital evolution has made huge volumes of data about music consumption available. Based on such data, we perform cross-era comparisons between Physical and Digital media within the music market in Brazil. First, we build artists' success time series to detect and characterize hot streak periods, defined as high-impact bursts that occur in sequence, in both eras. Then, we identify groups of artists with distinct success levels by applying a cluster analysis based on hot streaks' features. We find the same clusters for both Physical and Digital eras: Spike Hit Artists, Big Hit Artists, and Top Hit Artists. Our results reveal significant changes in the music industry dynamics over the years by identifying the core of each era

Early Profile Pruning on XML-aware Publish/Subscribe Systems

Publish-subscribe applications are an important class of contentbased dissemination systems where the message transmission is defined by the message content, rather than its destination IP address. With the increasing use of XML as the standard format on many Internet-based applications, XML aware pub-sub applications become necessary. In such systems, the messages (generated by publishers) are encoded as XML documents, and the profiles (defined by subscribers) as XML query statements. As the number of documents and query requests grow, the performance and scalability of the matching phase (i.e. matching of queries to incoming documents) become vital. Current solutions have limited or no flexibility to prune out queries in advance. In this paper, we overcome such limitation by proposing a novel early pruning approach called Bounding-based XML Filtering or BoXFilter. The BoXFilter is based on a new tree-like indexing structure that organizes the queries based on their similarity and provides lower and upper bound estimations needed to prune queries not related to the incoming documents. Our experimental evaluation shows that the early profile pruning approach offers drastic performance improvements over the current state-of-the-art in XML filtering. 1