1,720,959 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
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
A Zero Trust Hybrid Machine Learning Algorithms for Threat Detection and Prevention with Explainable Threat Intelligence.
Full text linkThis study presents a dual-model intelligent cybersecurity framework integrating Malware Detection and SQL Injection Detection to enhance automated threat identification and prevention. For malware detection, a Random Forest classifier was employed to analyze users activities. The model achieved an accuracy of 99.13%, precision of 98.52%, and recall of 98.56%, demonstrating exceptional reliability in differentiating malicious from benign files. The ROC curve (AUC = 0.9994) and Precision–Recall curve confirmed the model’s high discriminative power, while LIME and Permutation Feature Importance analyses provided interpretability, revealing that features like MajorSubsystemVersion and SectionsMeanEntropy strongly influence classification outcomes. For SQL injection detection, a feedforward neural network (FFNN) with two dense layers (32 and 64 neurons) was implemented using three handcrafted features—query length, punctuation, and SQL keywords. The model achieved an accuracy of 99.73%, precision of 99.7%, recall of 99.95%, and F1-score of 99.8%, indicating near- perfect discrimination between malicious and benign queries. The ROC (AUC = 1.00) and Precision–Recall curves further confirmed its robustness. LIME explanations provided local interpretability by highlighting influential query attributes driving predictions. A real-time detection dashboard continuously validates every access attempt—file uploads or SQL queries—using both models in parallel. Malicious inputs are instantly flagged and blocked, ensuring proactive protection. Overall, the proposed framework combines high detection accuracy with explainable artificial intelligence (XAI) techniques, providing both transparency and reliability for modern cybersecurity defense systems
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
Integrating Zero-Trust Architecture with Deep Learning Algorithm to Prevent Structured Query Language Injection Attack in Cloud Database
Full text linkThe increasing reliance on cloud databases has made them a prime target for cyber attacks, with Structured
Query Language (SQL) injection being a particularly devastating threat. SQL injection attacks pose significant
threats to database security, compromising sensitive information. Deep learning algorithms have emerged as
effective solutions to detect and prevent SQL injection attacks. This study proposes a novel approach to
detecting SQL injection attack by integrating deep learning-based detection with zero-trust architectute. The
proposed system utilizes a Feed-Forward Neural Network (FNN)to analyze database queries and detect potential
SQL injection attacks. The FNN model is trained on a dataset of labelled queries, allowing it to learn patterns
and anomalies indictive of SQL injection attacks. The output of the FNN model is then integrated with zero-
trust architecture, which enforces strict access controls and authentication mechanisms based on the results
generated by the FNN model. The model exhibits a precision score approximating 100% accuracy in the
classification of queries deemed normal, while achieving a 94% rate of correct classification for queries
indicative of SQL injection attacks. By leveraging advanced machine learning techniques, our approach aims to
identify and block malicious queries in real-time, ensuring the integrity and security of cloud-based data.
Through a comprehensive evaluation, we demonstrate the effectiveness of our deep learning-based solution with
zero-trust architecture in detecting SQL injection attacks with high accuracy and low false positives
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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