1,720,954 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
Підвищення точності виявлення м'яча у відео футбольних матчів за допомогою механізмів уваги в CNN-моделях на основі FPN
Full text linkWhile deep learning models have significantly advanced player detection in sports analytics, accurately identi fying the football remains a persistent challenge due to its small size, rapid movement, frequent occlusions, and visual similarity to other elements such as player socks, logos, and field markings. This limitation significantly reduces the effectiveness of automated systems in comprehensively analyzing football matches, particularly in applications such as tactical event recognition, shot classification, and game state prediction. In this paper, we propose a method to improve ball detection accuracy in football videos by enhancing an existing architecture based on Feature Pyramid Networks (FPN). The original FPN-based model, although efficient for detecting large-scale players, shows limited performance in detecting small objects such as the ball. To address this, we integrate lightweight attention mechanisms to help the model focus on more relevant spatial and semantic fea tures. Specifically, we introduce Squeeze-and-Excitation (SE) layers into the backbone of the network to perform channel-wise feature recalibration and embed a Convolutional Block Attention Module (CBAM) into the ball detection head to refine both spatial and channel-level attention. These modifications are designed to enhance the network’s ability to distinguish the ball from cluttered backgrounds and visually similar objects. Our exper iments, conducted on the ISSIA-CNR and Soccer Player Detection datasets, demonstrate that the proposed at tention-augmented model achieves improved ball classification accuracy compared to the baseline, with no deg radation in player detection performance. These results validate the utility of lightweight attention mechanisms in the context of small object detection and provide a promising direction for more robust and real-time football video analysis systems.Prombles in programming 2025; 2: 54-62 Попри значний прогрес у виявленні гравців завдяки моделям глибокого навчання в спортивній аналітиці, точне розпізнавання футбольного м’яча залишається складною задачею через його малий розмір, швидкий рух, часті оклюзії та візуальну подібність до інших елементів, таких як гетри гравців, логотипи та розмітка поля. Ці обмеження значно знижують ефективність автоматизованих систем для комплексного аналізу фу тбольних матчів, особливо в таких задачах, як розпізнавання тактичних подій, класифікація ударів і про гнозування ігрових станів. У цій роботі запропоновано метод підвищення точності виявлення м’яча у відео футбольних матчів шляхом удосконалення наявної архітектури на основі Feature Pyramid Networks (FPN). Базова модель на основі FPN, хоча й ефективна для виявлення гравців, демонструє обмежену продуктив ність у розпізнаванні дрібних об’єктів, таких як м’яч. Для вирішення цієї проблеми ми інтегрували легкі механізми уваги, які дозволяють моделі краще зосереджуватись на релевантних просторових та семантич них ознаках. Зокрема, ми впроваджуємо шари Squeeze-and-Excitation (SE) у базову мережу для переналаш тування ознак на рівні каналів, а також додаємо модуль CBAM (Convolutional Block Attention Module) до голови виявлення м’яча для уточнення просторової та канальної уваги. Ці модифікації покликані покра щити здатність мережі відрізняти м’яч від візуально схожих об’єктів і перевантаженого фону. Наші експе рименти, проведені на наборах даних ISSIA-CNR та Soccer Player Detection, демонструють, що запропоно вана модель з увагою досягає кращої точності класифікації м’яча порівняно з базовим підходом, без погір шення точності виявлення гравців. Отримані результати підтверджують ефективність легких механізмів уваги в задачах виявлення дрібних об’єктів та відкривають перспективи для створення більш надійних і реалістичних систем аналізу футбольних відео у реальному часі.Prombles in programming 2025; 2: 54-6
Enhancing ball detection in football videos using attention mechanisms in FPN-based CNNS
Full text linkWhile deep learning models have significantly advanced player detection in sports analytics, accurately identi fying the football remains a persistent challenge due to its small size, rapid movement, frequent occlusions, and visual similarity to other elements such as player socks, logos, and field markings. This limitation significantly reduces the effectiveness of automated systems in comprehensively analyzing football matches, particularly in applications such as tactical event recognition, shot classification, and game state prediction. In this paper, we propose a method to improve ball detection accuracy in football videos by enhancing an existing architecture based on Feature Pyramid Networks (FPN). The original FPN-based model, although efficient for detecting large-scale players, shows limited performance in detecting small objects such as the ball. To address this, we integrate lightweight attention mechanisms to help the model focus on more relevant spatial and semantic fea tures. Specifically, we introduce Squeeze-and-Excitation (SE) layers into the backbone of the network to perform channel-wise feature recalibration and embed a Convolutional Block Attention Module (CBAM) into the ball detection head to refine both spatial and channel-level attention. These modifications are designed to enhance the network’s ability to distinguish the ball from cluttered backgrounds and visually similar objects. Our exper iments, conducted on the ISSIA-CNR and Soccer Player Detection datasets, demonstrate that the proposed at tention-augmented model achieves improved ball classification accuracy compared to the baseline, with no deg radation in player detection performance. These results validate the utility of lightweight attention mechanisms in the context of small object detection and provide a promising direction for more robust and real-time football video analysis systems.Prombles in programming 2025; 2: 54-6
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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