1,720,958 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Machine learning with geo, temporal, textual, and visual data for real world applications
No full textThis thesis explores the use of multimodal machine learning models, incorporating geo, temporal, textual, and visual data to overcome the limitations of traditional unimodal approaches. By reflecting the complexity of human perception, it enhances real-world applications and helps tackle challenges such as urban event detection, object detection, and demand forecasting.Key research questions include how multimodal methods enhance applications using diverse data types, the optimal integration of modalities, and the role of contextual information. We examine the contributions of advances in natural language processing, forecasting, and computer vision, alongside the challenges of multimodal fusion in real-world contexts.The thesis is based on four publications. The first demonstrates how combining visual and textual data improves urban micro-event classification. The second introduces a system for collecting and analyzing street-level imagery to detect urban objects. The third discusses the GIGO dataset for classifying urban garbage, highlighting the need for multimodal approaches. The final chapter presents a multimodal product demand forecasting system, showcasing the Multimodal Temporal Fusion Transformer’s success in reducing food waste and improving predictions.Our findings underscore the importance of contextual information and techniques like representation learning in solving real-world problems with multimodal machine learning. The challenges faced in integrating diverse data types highlight the field’s potential to advance future real-world applications
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Machine learning with geo, temporal, textual, and visual data for real world applications
Full text linkThis thesis explores the use of multimodal machine learning models, incorporating geo, temporal, textual, and visual data to overcome the limitations of traditional unimodal approaches. By reflecting the complexity of human perception, it enhances real-world applications and helps tackle challenges such as urban event detection, object detection, and demand forecasting.Key research questions include how multimodal methods enhance applications using diverse data types, the optimal integration of modalities, and the role of contextual information. We examine the contributions of advances in natural language processing, forecasting, and computer vision, alongside the challenges of multimodal fusion in real-world contexts.The thesis is based on four publications. The first demonstrates how combining visual and textual data improves urban micro-event classification. The second introduces a system for collecting and analyzing street-level imagery to detect urban objects. The third discusses the GIGO dataset for classifying urban garbage, highlighting the need for multimodal approaches. The final chapter presents a multimodal product demand forecasting system, showcasing the Multimodal Temporal Fusion Transformer’s success in reducing food waste and improving predictions.Our findings underscore the importance of contextual information and techniques like representation learning in solving real-world problems with multimodal machine learning. The challenges faced in integrating diverse data types highlight the field’s potential to advance future real-world applications
Machine learning with geo, temporal, textual, and visual data for real world applications
Full text linkThis thesis explores the use of multimodal machine learning models, incorporating geo, temporal, textual, and visual data to overcome the limitations of traditional unimodal approaches. By reflecting the complexity of human perception, it enhances real-world applications and helps tackle challenges such as urban event detection, object detection, and demand forecasting.Key research questions include how multimodal methods enhance applications using diverse data types, the optimal integration of modalities, and the role of contextual information. We examine the contributions of advances in natural language processing, forecasting, and computer vision, alongside the challenges of multimodal fusion in real-world contexts.The thesis is based on four publications. The first demonstrates how combining visual and textual data improves urban micro-event classification. The second introduces a system for collecting and analyzing street-level imagery to detect urban objects. The third discusses the GIGO dataset for classifying urban garbage, highlighting the need for multimodal approaches. The final chapter presents a multimodal product demand forecasting system, showcasing the Multimodal Temporal Fusion Transformer’s success in reducing food waste and improving predictions.Our findings underscore the importance of contextual information and techniques like representation learning in solving real-world problems with multimodal machine learning. The challenges faced in integrating diverse data types highlight the field’s potential to advance future real-world applications
Machine learning with geo, temporal, textual, and visual data for real world applications
No full textThis thesis explores the use of multimodal machine learning models, incorporating geo, temporal, textual, and visual data to overcome the limitations of traditional unimodal approaches. By reflecting the complexity of human perception, it enhances real-world applications and helps tackle challenges such as urban event detection, object detection, and demand forecasting.Key research questions include how multimodal methods enhance applications using diverse data types, the optimal integration of modalities, and the role of contextual information. We examine the contributions of advances in natural language processing, forecasting, and computer vision, alongside the challenges of multimodal fusion in real-world contexts.The thesis is based on four publications. The first demonstrates how combining visual and textual data improves urban micro-event classification. The second introduces a system for collecting and analyzing street-level imagery to detect urban objects. The third discusses the GIGO dataset for classifying urban garbage, highlighting the need for multimodal approaches. The final chapter presents a multimodal product demand forecasting system, showcasing the Multimodal Temporal Fusion Transformer’s success in reducing food waste and improving predictions.Our findings underscore the importance of contextual information and techniques like representation learning in solving real-world problems with multimodal machine learning. The challenges faced in integrating diverse data types highlight the field’s potential to advance future real-world applications
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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