1,721,030 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
Detection of Body Shaming in Social Media: A Comparative Study of Traditional Machine Learning and Transformer-based Models
Full text linkWith social media becoming a significant part of the daily life among people of all ages, people are also faced with harmful contents which could have a negative impact on their wellness. Body shaming is one such area where people especially younger generation are affected.
This study investigates and compares the performance of traditional machine learning models and state-of-the-art transformer-based pre-trained language models in identifying and classifying body shaming content from textual social media data. Two datasets were used: a 4k dataset consolidated from existing sources, and a larger 6k dataset extended with novel data collected from TikTok and X.
The traditional models evaluated include Support Vector Machines (SVM), Logistic Regression, Naive Bayes, Random Forests, XGBoost, and AdaBoost, while the pre-trained language models explored were BERT, RoBERTa, and XLNet. Models were trained and evaluated on both datasets, with performance assessed using metrics such as precision, recall, F1-score, and Matthews Correlation Coefficient (MCC).
Results demonstrated the better performance of pre-trained language models over traditional models, particularly on the larger 6k dataset. BERT exhibited the highest F1-score (0.8062) and MCC (0.7631) on the 6k dataset, while RoBERTa performed best on the 4k dataset with an F1-score of 0.8494 and MCC of 0.8048. Among traditional models, SVM and Random Forests performed well, with Random Forests achieving the highest F1-score (0.7095) and MCC (0.6644) on the 6k dataset. This study highlights the effectiveness of pre-trained language models in capturing complex linguistic patterns and semantics, enabling better generalization to larger and more imbalanced datasets. However, traditional models like SVM and Random Forests remain viable alternatives, particularly in resource-constrained environments or when dealing with smaller datasets
Can XAI-guided Point-of-Interest Proposal and SAM Perform Crossmodal Food Image Segmentation?
Full text linkFood image segmentation is a task with multiple problems. There are limited food image segmentation datasets, considerable variability in the types of food available across geographical regions and ethnicities, and adapting to new modalities and domains can be resource intensive.
In this thesis, we explore the use of methods from explainable AI as a way to give new modalities general-purpose segmentation models. It achieves this by using CLIP as a multimodal model with a shared embedding space for text and image data. We propose a system with modular components that integrate to form a promptable text to image segmentation model. Through an extensive set of hyperparameter impact studies, we evaluate how the system performs under different configurations, and determine which components have the highest impact on the system’s performance.
We found that using techniques from explainable AI together with filtering techniques and the sampling of points of interest to be highly effective in guiding a general-purpose segmentation model. While we did not beat state of the art models, our results were reasonably competitive. However, we had problems developing a classifier for dishes and ingredients using high-dimensional embeddings and k-nearest neighbors search.
In conclusion, the research proved interesting, and deserves further research. We were able to segment food images under supervision. However, our classification results did not prove fruitful in this thesis. We conjecture that using a stronger multimodal model like SigLiT could improve our results. Additionally, both the classification and segmentation tasks could be improved by fine-tuning the respective component on a large food-specific dataset like Recipe1M
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
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
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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