1,720,954 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
Get PDFWe 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
Driver prediction model for autonomous vehicle within a virtual testing platform tailored to Malaysia driving scenarios
Get PDFVirtual simulation is a vital tool for testing autonomous vehicle (AV) systems in hazardous scenarios due to its cost-effectiveness, reproducibility, and safety. The reliability of such simulations depends on the accuracy of vehicle dynamics, environmental models, and, critically, driver models, which must replicate human driving behaviour to ensure valid testing results. This study develops an artificial intelligence-based driver model tailored to the Malaysian driving environment, addressing significant differences in traffic behaviour between developing and developed countries. To achieve this, a non-linear 14 Degrees of Freedom (DOF) vehicle model was developed and validated through comparative analysis with experimental data to ensure accurate replication of vehicle handling characteristics. Real-world driving data were collected over 245 hours using an instrumented vehicle equipped with cost-effective off-the-shelf sensors, covering diverse road networks, including urban, rural, and highway scenarios. Additionally, a mixed-reality driving simulator, integrating IPG CarMaker with a 6-degree-of-freedom motion platform and virtual reality, was employed to capture realistic human driving behaviours. Thirty participants were invited, and their driving styles were classified into aggressive, normal, and slow categories. The model was trained using normal driver data to develop a baseline for human-like driving behaviour. A hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model, incorporating attention mechanisms to capture spatial and temporal dependencies in driving behaviour, was implemented. The model achieved 84.63% accuracy in predicting steering, throttle, and braking inputs under simulated conditions. However, when tested with real-world data, accuracy declined to 67.23%, highlighting a generalization gap due to underrepresented road types, varying time-of-day conditions, and environmental factors such as weather variations. To mitigate this issue, further training was conducted using a combination of real-world and simulation data, improving the model’s adaptability. The proposed driver model was benchmarked against existing deep learning-based driver models, demonstrating superior performance in replicating human-like driving behaviour within the Malaysian driving context. Despite its contributions, the study acknowledges limitations in data collection, including the limited number of participants, relatively short driving durations per driver, and insufficient representation of extreme driving behaviours. These constraints impact the generalizability of the model to all traffic scenarios. Future work should focus on expanding the dataset with more diverse driving conditions and optimizing the model to enhance its robustness in real-world applications. This research advances driver modelling by leveraging deep learning to create a more contextually relevant model for Malaysia, bridging the gap between virtual simulation and real-world driving behaviour. The developed model has significant implications for AV testing, driver training systems, and intelligent transportation applications in developing countries with complex driving environments
Driver prediction model for autonomous vehicle within a virtual testing platform tailored to Malaysia driving scenarios
Get PDFVirtual simulation is a vital tool for testing autonomous vehicle (AV) systems in hazardous scenarios due to its cost-effectiveness, reproducibility, and safety. The reliability of such simulations depends on the accuracy of vehicle dynamics, environmental models, and, critically, driver models, which must replicate human driving behaviour to ensure valid testing results. This study develops an artificial intelligence-based driver model tailored to the Malaysian driving environment, addressing significant differences in traffic behaviour between developing and developed countries. To achieve this, a non-linear 14 Degrees of Freedom (DOF) vehicle model was developed and validated through comparative analysis with experimental data to ensure accurate replication of vehicle handling characteristics. Real-world driving data were collected over 245 hours using an instrumented vehicle equipped with cost-effective off-the-shelf sensors, covering diverse road networks, including urban, rural, and highway scenarios. Additionally, a mixed-reality driving simulator, integrating IPG CarMaker with a 6-degree-of-freedom motion platform and virtual reality, was employed to capture realistic human driving behaviours. Thirty participants were invited, and their driving styles were classified into aggressive, normal, and slow categories. The model was trained using normal driver data to develop a baseline for human-like driving behaviour. A hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model, incorporating attention mechanisms to capture spatial and temporal dependencies in driving behaviour, was implemented. The model achieved 84.63% accuracy in predicting steering, throttle, and braking inputs under simulated conditions. However, when tested with real-world data, accuracy declined to 67.23%, highlighting a generalization gap due to underrepresented road types, varying time-of-day conditions, and environmental factors such as weather variations. To mitigate this issue, further training was conducted using a combination of real-world and simulation data, improving the model’s adaptability. The proposed driver model was benchmarked against existing deep learning-based driver models, demonstrating superior performance in replicating human-like driving behaviour within the Malaysian driving context. Despite its contributions, the study acknowledges limitations in data collection, including the limited number of participants, relatively short driving durations per driver, and insufficient representation of extreme driving behaviours. These constraints impact the generalizability of the model to all traffic scenarios. Future work should focus on expanding the dataset with more diverse driving conditions and optimizing the model to enhance its robustness in real-world applications. This research advances driver modelling by leveraging deep learning to create a more contextually relevant model for Malaysia, bridging the gap between virtual simulation and real-world driving behaviour. The developed model has significant implications for AV testing, driver training systems, and intelligent transportation applications in developing countries with complex driving environments
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe 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
- …
