15,995 research outputs found
Deliberative Democracy, Perspective from Indo-Pacific Blogosphere: A Survey
Deliberation and communication within the national space have had numerous
implications on how citizens online and offline perceive government. It has
also impacted the relationship between opposition and incumbent governments in
the Indo-Pacific region. Authoritarian regimes have historically had control
over the dissemination of information, thereby controlling power and limiting
challenges from citizens who are not comfortable with the status quo. Social
media and blogs have allowed citizens of these countries to find a way to
communicate, and the exchange of information continues to rise. The quest by
both authoritarian and democratic regimes to control or influence the
discussion in the public sphere has given rise to concepts like cybertroopers,
congressional bloggers, and commentator bloggers, among others. Cybertroopers
have become the de facto online soldiers of authoritarian regimes who must
embrace democracy. While commentator and congressional bloggers have acted with
different strategies, commentator bloggers educate online citizens with
knowledgeable information to influence the citizens. Congressional bloggers are
political officeholders who use blogging to communicate their positions on
ongoing national issues. Therefore, this work has explored various concepts
synonymous with the Indo-Pacific public sphere and how it shapes elections and
democracy
Grasping nothing: a study of minimal ontologies and the sense of music
If music were to have a proper sense – one in which it is truly given – one might reasonably place this in sound and aurality. I contend, however, that no such sense exists; rather, the sense of music takes place, and it does so with the impossible. To this end, this thesis – which is a work of philosophy and music – advances an ontology of the impossible (i.e., it thinks the being of what, properly speaking, can have no being) and considers its implications for music, articulating how ontological aporias – of the event, of thinking the absolute, and of sovereignty’s dismemberment – imply senses of music that are anterior to sound. John Cage’s Silent Prayer, a nonwork he never composed, compels a rerethinking of silence on the basis of its contradictory status of existence; Florian Hecker et al.’s Speculative Solution offers a basis for thinking absolute music anew to the precise extent that it is a discourse of meaninglessness; and Manfred Werder’s [yearn] pieces exhibit exemplarily that music’s sense depends on the possibility of its counterfeiting. Inso-much as these accounts produce musical senses that take the place of sound, they are also understood to be performances of these pieces. Here, then, thought is music’s organon and its instrument
Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review
In this paper, a critical bibliometric analysis study is conducted, coupled
with an extensive literature survey on recent developments and associated
applications in machine learning research with a perspective on Africa. The
presented bibliometric analysis study consists of 2761 machine learning-related
documents, of which 98% were articles with at least 482 citations published in
903 journals during the past 30 years. Furthermore, the collated documents were
retrieved from the Science Citation Index EXPANDED, comprising research
publications from 54 African countries between 1993 and 2021. The bibliometric
study shows the visualization of the current landscape and future trends in
machine learning research and its application to facilitate future
collaborative research and knowledge exchange among authors from different
research institutions scattered across the African continent
Generalized Weak Supervision for Neural Information Retrieval
Neural ranking models (NRMs) have demonstrated effective performance in
several information retrieval (IR) tasks. However, training NRMs often requires
large-scale training data, which is difficult and expensive to obtain. To
address this issue, one can train NRMs via weak supervision, where a large
dataset is automatically generated using an existing ranking model (called the
weak labeler) for training NRMs. Weakly supervised NRMs can generalize from the
observed data and significantly outperform the weak labeler. This paper
generalizes this idea through an iterative re-labeling process, demonstrating
that weakly supervised models can iteratively play the role of weak labeler and
significantly improve ranking performance without using manually labeled data.
The proposed Generalized Weak Supervision (GWS) solution is generic and
orthogonal to the ranking model architecture. This paper offers four
implementations of GWS: self-labeling, cross-labeling, joint cross- and
self-labeling, and greedy multi-labeling. GWS also benefits from a query
importance weighting mechanism based on query performance prediction methods to
reduce noise in the generated training data. We further draw a theoretical
connection between self-labeling and Expectation-Maximization. Our experiments
on two passage retrieval benchmarks suggest that all implementations of GWS
lead to substantial improvements compared to weak supervision in all cases
Interval Type-2 Beta Fuzzy Near Sets Approach to Content-Based Image Retrieval
In computer-based search systems, similarity plays a key role in replicating the human search process. Indeed, the human search process underlies many natural abilities such as image recovery, language comprehension, decision making, or pattern recognition. The search for images consists of establishing a correspondence between the available image and that sought by the user, by measuring the similarity between the images. Image search by content is generaly based on the similarity of the visual characteristics of the images. The distance function used to evaluate the similarity between images depends notonly on the criteria of the search but also on the representation of the characteristics of the image. This is the main idea of a content-based image retrieval (CBIR) system. In this article, first, we constructed type-2 beta fuzzy membership of descriptor vectors to help manage inaccuracy and uncertainty of characteristics extracted the feature of images. Subsequently, the retrieved images are ranked according to the novel similarity measure, noted type-2 fuzzy nearness measure (IT2FNM). By analogy to Type-2 Fuzzy Logic and motivated by near sets theory, we advanced a new fuzzy similarity measure (FSM) noted interval type-2 fuzzy nearness measure (IT-2 FNM). Then, we proposed three new IT-2 FSMs and we have provided mathematical justification to demonstrate that the proposed FSMs satisfy proximity properties (i.e. reflexivity, transitivity, symmetry, and overlapping). Experimental results generated using three image databases showing consistent and significant results
Um modelo para suporte automatizado ao reconhecimento, extração, personalização e reconstrução de gráficos estáticos
Data charts are widely used in our daily lives, being present in regular media,
such as newspapers, magazines, web pages, books, and many others. A well constructed
data chart leads to an intuitive understanding of its underlying data
and in the same way, when data charts have wrong design choices, a redesign
of these representations might be needed. However, in most cases, these
charts are shown as a static image, which means that the original data are not
usually available. Therefore, automatic methods could be applied to extract the
underlying data from the chart images to allow these changes. The task of
recognizing charts and extracting data from them is complex, largely due to the
variety of chart types and their visual characteristics.
Computer Vision techniques for image classification and object detection are
widely used for the problem of recognizing charts, but only in images without
any disturbance. Other features in real-world images that can make this task
difficult are not present in most literature works, like photo distortions, noise,
alignment, etc. Two computer vision techniques that can assist this task and
have been little explored in this context are perspective detection and
correction. These methods transform a distorted and noisy chart in a clear
chart, with its type ready for data extraction or other uses. The task of
reconstructing data is straightforward, as long the data is available the
visualization can be reconstructed, but the scenario of reconstructing it on the
same context is complex.
Using a Visualization Grammar for this scenario is a key component, as these
grammars usually have extensions for interaction, chart layers, and multiple
views without requiring extra development effort.
This work presents a model for automated support for custom recognition, and
reconstruction of charts in images. The model automatically performs the
process steps, such as reverse engineering, turning a static chart back into its
data table for later reconstruction, while allowing the user to make modifications
in case of uncertainties. This work also features a model-based architecture
along with prototypes for various use cases. Validation is performed step by
step, with methods inspired by the literature. This work features three use
cases providing proof of concept and validation of the model.
The first use case features usage of chart recognition methods focused on
documents in the real-world, the second use case focus on vocalization of
charts, using a visualization grammar to reconstruct a chart in audio format,
and the third use case presents an Augmented Reality application that
recognizes and reconstructs charts in the same context (a piece of paper)
overlaying the new chart and interaction widgets. The results showed that with
slight changes, chart recognition and reconstruction methods are now ready for
real-world charts, when taking time, accuracy and precision into consideration.Os gráficos de dados são amplamente utilizados na nossa vida diária, estando
presentes nos meios de comunicação regulares, tais como jornais, revistas,
páginas web, livros, e muitos outros. Um gráfico bem construÃdo leva a uma
compreensão intuitiva dos seus dados inerentes e da mesma forma, quando
os gráficos de dados têm escolhas de conceção erradas, poderá ser
necessário um redesenho destas representações. Contudo, na maioria dos
casos, estes gráficos são mostrados como uma imagem estática, o que
significa que os dados originais não estão normalmente disponÃveis. Portanto,
poderiam ser aplicados métodos automáticos para extrair os dados inerentes
das imagens dos gráficos, a fim de permitir estas alterações. A tarefa de
reconhecer os gráficos e extrair dados dos mesmos é complexa, em grande
parte devido à variedade de tipos de gráficos e à s suas caracterÃsticas visuais.
As técnicas de Visão Computacional para classificação de imagens e deteção
de objetos são amplamente utilizadas para o problema de reconhecimento de
gráficos, mas apenas em imagens sem qualquer ruÃdo. Outras caracterÃsticas
das imagens do mundo real que podem dificultar esta tarefa não estão
presentes na maioria das obras literárias, como distorções fotográficas, ruÃdo,
alinhamento, etc. Duas técnicas de visão computacional que podem ajudar
nesta tarefa e que têm sido pouco exploradas neste contexto são a deteção e
correção da perspetiva. Estes métodos transformam um gráfico distorcido e
ruidoso em um gráfico limpo, com o seu tipo pronto para extração de dados
ou outras utilizações. A tarefa de reconstrução de dados é simples, desde que
os dados estejam disponÃveis a visualização pode ser reconstruÃda, mas o
cenário de reconstrução no mesmo contexto é complexo.
A utilização de uma Gramática de Visualização para este cenário é um
componente chave, uma vez que estas gramáticas têm normalmente
extensões para interação, camadas de gráficos, e visões múltiplas sem exigir
um esforço extra de desenvolvimento.
Este trabalho apresenta um modelo de suporte automatizado para o
reconhecimento personalizado, e reconstrução de gráficos em imagens
estáticas. O modelo executa automaticamente as etapas do processo, tais
como engenharia inversa, transformando um gráfico estático novamente na
sua tabela de dados para posterior reconstrução, ao mesmo tempo que
permite ao utilizador fazer modificações em caso de incertezas. Este trabalho
também apresenta uma arquitetura baseada em modelos, juntamente com
protótipos para vários casos de utilização. A validação é efetuada passo a
passo, com métodos inspirados na literatura. Este trabalho apresenta três
casos de uso, fornecendo prova de conceito e validação do modelo.
O primeiro caso de uso apresenta a utilização de métodos de reconhecimento
de gráficos focando em documentos no mundo real, o segundo caso de uso
centra-se na vocalização de gráficos, utilizando uma gramática de visualização
para reconstruir um gráfico em formato áudio, e o terceiro caso de uso
apresenta uma aplicação de Realidade Aumentada que reconhece e reconstrói
gráficos no mesmo contexto (um pedaço de papel) sobrepondo os novos
gráficos e widgets de interação. Os resultados mostraram que com pequenas
alterações, os métodos de reconhecimento e reconstrução dos gráficos estão
agora prontos para os gráficos do mundo real, tendo em consideração o
tempo, a acurácia e a precisão.Programa Doutoral em Engenharia Informátic
Exploring Topics in Bibliometric Research Through Citation Networks and Semantic Analysis.
This article surveys topic distributions of the academic literature that employs the terms bibliometrics, scientometrics, and informetrics. This exploration allows informing on the adoption of those terms and publication patterns of the authors acknowledging their work to be part of bibliometric research. We retrieved 20,268 articles related to bibliometrics and applied methodologies that exploit various features of the dataset to surface different topic representations. Across them, we observe major trends including discussions on theory, regional publication patterns, databases, and tools. There is a great increase in the application of bibliometrics as science mapping and decision-making tools in management, public health, sustainability, and medical fields. It is also observed that the term bibliometrics has reached an overall generality, while the terms scientometrics and informetrics may be more accurate in representing the core of bibliometric research as understood by the information and library science field. This article contributes by providing multiple snapshots of a field that has grown too quickly beyond the confines of library science
Photography and Aesthetics: a critical study on visual and textual narratives in the lifework of Sergio LarraÃn and its impact in 20th century Europe and Latin America
The main focus of this study is a theoretical exploration of critical approaches applicable to the work of the Chilean photographer Sergio LarraÃn (1931-2012). It presents analytical tools to contextualise and understand the importance and impact of his work in photographic studies and his portrayal of twentieth-century Latin American and European culture. It inspects in depth a large portion of his photo work, which is still only partially published and mostly reduced to his "active" period as a photojournalist, aside from the personal photographic exploration of his early and late career (C. Mena). This extended material creates a broader scope for understanding his photographs and him as a canonical photographer. This study analyses the photographer's trajectory as discourses of recollection of historical memory in time (Mauad) to trace LarraÃn's collective memory associated with his visual production. Such analysis helps decode his visual imagery and his projection and impact on the European and Latin American culture. This strategy helps solve a two fold problem: firstly, it generates an interpretive consistency to understand the Chilean's photographic practice; secondly, it explores the power of images as an aesthetic experience in the installation of nationalist ideologies and the creation of imaginaries (B. Anderson 163)
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